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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240624T080000
DTEND;TZID=America/Los_Angeles:20240703T170000
DTSTAMP:20260423T151137
CREATED:20250424T060230Z
LAST-MODIFIED:20250424T060230Z
UID:6428-1719216000-1720026000@css.pre2.ss.ucla.edu
SUMMARY:The Summer Institutes in Computational Social Science (SICSS) 2024
DESCRIPTION:From June 24 to July 3\, 2024 the University of California\, Los Angeles (UCLA) Division of Social Sciences and the California Center for Population Research will sponsor the Summer Institute in Computational Social Science\, to be held at the University of California Los Angeles. \nThe Organizing Committee\nJennie Brand\, Professor\, Sociology and Statistics\nDora Costa\, Professor\, Economics\nPatrick Heuveline\, Professor\, Sociology\, and International Institute\nRandall Kuhn\, Professor\, Community Health Sciences \nFor more information about the event go here: https://sicss.io/2024/ucla/
URL:https://css.pre2.ss.ucla.edu/event/the-summer-institutes-in-computational-social-science-sicss-2024/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Conference,CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240522T130000
DTEND;TZID=America/Los_Angeles:20240522T160000
DTSTAMP:20260423T151137
CREATED:20250424T055814Z
LAST-MODIFIED:20250424T055814Z
UID:6423-1716382800-1716393600@css.pre2.ss.ucla.edu
SUMMARY:Peter Hull\, Brown University\, “Formula Instruments” (STC Workshop)
DESCRIPTION:Biography: Peter Hull is a Professor of Economics at Brown University\, a Faculty Research Fellow in the NBER Labor Studies\, Education\, and Health Care programs in Labor Studies\, and the econometrics editor at the Review of Economics and Statistics. His research spans a variety of topics in applied econometrics\, education\, health care\, discrimination\, and criminal justice. He was awarded an Alfred P. Sloan Research Fellowship in 2023 in recognition of this work. \nFormula Instruments\nAbstract: Many studies in economics use instruments or treatments which combine a set of exogenous shocks with other predetermined variables by a known formula. Examples include shift-share instruments\, measures of social or spatial spillovers\, and treatments capturing eligibility for a public policy. This workshop reviews recent econometric tools for this setting\, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. Practical insights will be illustrated with two empirical applications and a coding lab.
URL:https://css.pre2.ss.ucla.edu/event/peter-hull-brown-university-formula-instruments-stc-workshop/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar,CCPR Workshop,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240515T130000
DTEND;TZID=America/Los_Angeles:20240515T160000
DTSTAMP:20260423T151137
CREATED:20250424T055550Z
LAST-MODIFIED:20250424T055550Z
UID:6416-1715778000-1715788800@css.pre2.ss.ucla.edu
SUMMARY:Christopher Walters\, University of California\, Berkeley (STC Workshop)\, Title: Empirical Bayes and large-scale inference
DESCRIPTION:Biography: Christopher Walters is an Associate Professor of Economics in the Department of Economics at the University of California\, Berkeley. Dr. Walters joined the faculty at Berkeley after completing his PhD in economics at MIT in 2013. He is also a Research Associate in the NBER programs on education and labor studies\, an IZA Research Fellow and an affiliate of JPAL-North America and MIT’s Blueprint Labs. His academic research focuses on topics in labor economics\, the economics of education\, and applied econometrics\, including work on school choice\, early childhood programs\, methods for evaluating school quality\, experimental measurement of labor market discrimination\, causal inference\, and empirical Bayes methods. \nAbstract: This workshop will cover empirical Bayes methods for studying heterogeneity\, estimating individual effects\, and making decisions in settings with many unit-specific parameters. Examples include studies of school\, teacher\, and physician quality; neighborhood effects on economic mobility; firm effects on wages; employer-specific labor market discrimination; and individualized treatment effect predictions and policy recommendations. Topics will include methods for quantifying variation in effects\, empirical Bayes shrinkage\, connections to machine learning methods\, and large-scale inference tools for multiple testing and decision-making. The lecture will be accompanied by coding examples.
URL:https://css.pre2.ss.ucla.edu/event/christopher-walters-university-of-california-berkeley-stc-workshop-title-empirical-bayes-and-large-scale-inference/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar,CCPR Workshop,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240213T150000
DTEND;TZID=America/Los_Angeles:20240213T160000
DTSTAMP:20260423T151137
CREATED:20250424T054428Z
LAST-MODIFIED:20250424T054650Z
UID:6413-1707836400-1707840000@css.pre2.ss.ucla.edu
SUMMARY:Development workshop\, 2/13 at 3pm “Scientific Accountability and Data Production”
DESCRIPTION:A panel discussion about open science\, ethical risks\, and potential drawbacks for certain forms of knowledge production with Irene Bloemraad (1)\, Cecilia Menjivar (2)\, Zachary Steinert-Threlkeld (3)\, and Jennifer Wagman (4)/ \n(1) UC Berkeley Sociology\, (2) UCLA Sociology\, (3) UCLA Luskin School of Public Affairs\, (4) UCLA Fielding School of Public Health
URL:https://css.pre2.ss.ucla.edu/event/development-workshop-2-13-at-3pm-scientific-accountability-and-data-production/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar,CCPR Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231101T120000
DTEND;TZID=America/Los_Angeles:20231101T131500
DTSTAMP:20260423T151137
CREATED:20250424T003610Z
LAST-MODIFIED:20250424T003610Z
UID:6405-1698840000-1698844500@css.pre2.ss.ucla.edu
SUMMARY:Computing Orientation Workshop with Neal Fultz (STC workshop)
DESCRIPTION:Instructor: Neal Fultz \nThis workshop has two halves. In the first half\, we will dive into the 3 main computing resources that CCPR offers to affiliates\, including it’s remote and on campus offerings. At the end of the first half\, we’ll get participants signed up for hoffman2 and TS2. Once signed up\, you’ll have state of the art hardware resources and most software you’ll ever need for demographic research. In the second half\, we’ll walk through how to use these computing resources\, identifying what resource is better to use for different computing project scenarios.
URL:https://css.pre2.ss.ucla.edu/event/computing-orientation-workshop-with-neal-fultz-stc-workshop/
CATEGORIES:CCPR Seminar,CCPR Workshop,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231011T120000
DTEND;TZID=America/Los_Angeles:20231011T131500
DTSTAMP:20260423T151137
CREATED:20250423T235557Z
LAST-MODIFIED:20250424T054822Z
UID:6398-1697025600-1697030100@css.pre2.ss.ucla.edu
SUMMARY:Gary Solon\, University of Michigan\, “What Are We Weighting For?” (STC Workshop)
DESCRIPTION:Biography:\nGary Solon is Professor Emeritus of Economics at the University of Michigan. He was Eller Professor of Economics at the University of Arizona during 2015-2018 and Professor of Economics at Michigan State University during 2007-2015. He is a research associate at the National Bureau of Economic Research\, a fellow of the Society of Labor Economists\, and a member of the Conference on Research in Income and Wealth. His research includes studies of family and community background effects on socioeconomic status\, earnings dynamics over the life cycle\, cyclical fluctuations in the labor market\, and microeconometric methods. \nWhat Are We Weighting For?\nAbstract: \nThe purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research\, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type\, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity\, (2) to achieve consistent estimates by correcting for endogenous sampling\, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case\, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting\, and we suggest methods for appropriate inference.
URL:https://css.pre2.ss.ucla.edu/event/gary-solon-university-of-michigan-what-are-we-weighting-for-stc-workshop/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar,CCPR Workshop,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230823T080000
DTEND;TZID=America/Los_Angeles:20230823T170000
DTSTAMP:20260423T151137
CREATED:20250423T235025Z
LAST-MODIFIED:20250424T003354Z
UID:6395-1692777600-1692810000@css.pre2.ss.ucla.edu
SUMMARY:UC Carpentries Fall Workshop Series: Coding and Data Management (virtual)
DESCRIPTION:Attend as many or as few sessions as you wish \n\n\nThis free\, virtual workshop is designed for researchers and enables non-experts to develop computing skills for research analysis. Registration is open to all UC students\, staff\, postdocs\, and faculty. \nTime: 8:30 – 12:30 PDT via Zoom \nWeek 1 \nSept. 11 / Day 1: The Unix Shell\nSept. 12 / Day 2: Git Version Control\nSept. 13 / Day 3: R (part 1)\nSept. 14 / Day 4: R (part 2) \nWeek 2 \nSept. 18/Day 5: Python (part 1)\nSept. 19/Day 6: Python (part 2)\nSept. 20/Day 7: Tidy Data\nSept. 21/Day 8: SQL \nTo register for this event visit: https://ti.to/ucsd-carpentries/uc-carpentries-fall-workshop-2023 \nWorkshop Details \nWhat is this workshop? \nAn 8-Day Carpentries workshop that aims to teach participants basic concepts\, skills\, and tools for R in RStudio\, Python in Jupyter Notebook\, an introduction to the Unix Shell\, Version Control with Git\, SQL\, and Tidy Data. This workshop is designed for researchers and enables non-experts to develop computing skills for research analysis. This is a free workshop. \nWho can participate? \nThe workshop is open to all University of California students\, staff\, postdocs and faculty. \nCurriculum: \n\nIntroduction to Unix Shell: learn the basics of command line interface and about navigating and working within files and directories\nVersion Control with Git: learn how to manage work\, edit code\, and collaborate on a team project in a repository\nIntroduction to R: learn basic coding\, concepts\, how to access data\, and use functions for data analysis using a RStudio\nIntroduction to Python: learn basic coding\, concepts\, how to access data\, and use functions for data analysis using a web-based application – Jupyter Notebook.\nTidy Data: learn how to use this tool to clean\, transform\, and track changes made to data.\nSQL: learn about relational databases\, how to import data\, and how to run basic SQL queries.\n\nThis workshop is co-hosted and supported by efforts from UC San Diego\, UC Los Angeles\, UC Merced\, UC Riverside\, UC San Francisco\, UC Santa Barbara and UC Berkeley. \nQuestions? \nContact Elizabeth McAulay\, UCLA Library\, at emcaulay@library.ucla.edu.
URL:https://css.pre2.ss.ucla.edu/event/uc-carpentries-fall-workshop-series-coding-and-data-management-virtual/
CATEGORIES:CCPR Seminar,CCPR Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230619T080000
DTEND;TZID=America/Los_Angeles:20230630T170000
DTSTAMP:20260423T151137
CREATED:20250424T062542Z
LAST-MODIFIED:20250424T062542Z
UID:6447-1687161600-1688144400@css.pre2.ss.ucla.edu
SUMMARY:Summer Institute in Computational Social Science
DESCRIPTION:From June 19 to June 30\, 2023\, the University of California\, Los Angeles (UCLA) Division of Social Sciences and the California Center for Population Research will sponsor the Summer Institute in Computational Social Science\, to be held at the University of California Los Angeles. The purpose of the Summer Institute is to bring together advanced Ph.D. students\, postdoctoral researchers\, and faculty interested in computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived). \nThe instructional program will involve lectures\, group problem sets\, and participant-led research projects\, with a special emphasis on causal inference. There will also be outside speakers who conduct computational social science research in a variety of settings\, such as academia\, industry\, and government. Topics covered include causal inference with observational data\, text analysis\, network analysis\, survey experiments\, and machine learning. There will be ample opportunities for participants to discuss their ideas and research with the organizers\, other participants\, and visiting speakers. Because we are committed to open and reproducible research\, all materials created by faculty and participants for the Summer Institute will be released open source. \nParticipation is restricted to advanced Ph.D. students\, postdoctoral researchers\, and junior faculty (within 7 years of their Ph.D). We welcome applicants from all backgrounds and fields of study\, especially junior faculty from neighboring institutions near Los Angeles. About 25-30 participants will be invited\, and participants are expected to fully attend and participate in the entire two-week program. \nApplication materials are due April 7\, 2023.
URL:https://css.pre2.ss.ucla.edu/event/summer-institute-in-computational-social-science/
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T120000
DTEND;TZID=America/Los_Angeles:20230524T133000
DTSTAMP:20260423T151137
CREATED:20250424T062406Z
LAST-MODIFIED:20250424T062406Z
UID:6444-1684929600-1684935000@css.pre2.ss.ucla.edu
SUMMARY:Difference-In-Difference Panel Discussion and Mini Conference
DESCRIPTION:Differences-in-differences Mini-conference \nMay 24\, 2023  \nUCLA\, California Center for Population Research \n9-11:30am Speakers hold for meetings \n12-1:30pm [CCPR seminar slot] Panel discussion: What’s new with differences-in-differences?  \nAndrew Goodman-Bacon (Minneapolis Federal Reserve Bank)\, Alyssa Bilinski (Brown)\, Jon Roth (Brown)\, Pedro Sant’Anna (Vanderbilt)\, Jeff Wooldridge (MSU) \nSHORT LUNCH BREAK & ROOM SET UP \n2:15-3:00pm Andrew Goodman-Bacon (Minneapolis Federal Reserve Bank)\, Pedro Sant’Anna (Vanderbilt) \n“Difference-in-Differences with a Continuous Treatments” \nThis paper analyzes difference-in-differences setups with a continuous treatment. We show that treatment effect on the treated-type parameters can be identified under a generalized parallel trends assumption that is similar to the binary treatment setup. However\, interpreting differences in these parameters across different values of the treatment can be particularly challenging due to treatment effect heterogeneity. We discuss alternative\, typically stronger\, assumptions that alleviate these challenges. We also provide a variety of treatment effect decomposition results\, highlighting that parameters associated with popular two-way fixed-effect specifications can be hard to interpret\, even when there are only two time periods. We introduce alternative estimation strategies that do not suffer from these drawbacks. Our results also cover cases where (i) there is no available untreated comparison group and (ii) there are multiple periods and variation in treatment timing\, which are both common in empirical work. \n3:00-3:45pm Alyssa Bilinski (Brown) \n“Parallel Trends in an Unparalleled Pandemic: Difference-in-Differences for Infectious Disease Policy Evaluation”  \nOver the course of the COVID-19 pandemic\, researchers have extensively studied the impact of public health interventions\, such as stay-at-home orders and mask policies\, on disease incidence and mortality.  Many policy evaluations employ a difference-in-differences (DiD) design\, which assumes that treatment and non-experimental comparison groups would have moved in parallel in expectation\, absent the intervention (the “parallel trends assumption”).  While researchers have used different specifications to capture potential non-linearities\, the plausibility of these specifications in the context of dynamic infection transmission is less well-understood.  Our work bridges this gap by formalizing epidemiological assumptions required for different DiD specifications\, positing an underlying susceptible\, infectious\, recovered (SIR) model.  We first explore common DiD specifications\, demonstrating that these often imply strict epidemiological assumptions.  For example\, DiD modeling raw cases or deaths as an outcome will be biased unless treatment and comparison groups have the same initial conditions\, susceptible fraction\, and transmission rate (“force of infection”); using a log transformation allows for different initial conditions\, but requires equal transmission rates and and susceptible fractions among groups.  Furthermore\, even if estimates are unbiased\, both specifications are often highly anti-conservative under standard error assumptions of a stochastic agent-based SIR model.  We then present more robust alternatives\, including modeling log difference as the primary outcome and modeling the causal effect of an intervention on the effective reproduction number\, rather than cases or deaths.  We demonstrate the implications of this work reanalyzing prior work on school mask policies. \n3:45pm Coffee break \n4-4:45pm Jeff Woodridge (MSU) \n“Estimating Distributional Treatment Effects with Staggered Interventions for Panel Data” \nI propose simple\, parametric approaches for estimating distributions of potential outcomes in a staggered difference-in-differences setting. The approach relies on versions of no anticipation and parallel trends assumptions. Estimators include imputation estimators or pooled maximum likelihood estimation. The approach can be applied to discrete\, continuous\, and mixed outcomes. A leading application is estimating quantile treatment effects in staggered DiD settings for a continuous outcome. \n4:45-5:30pm Jonathan Roth (Brown) \n“Log-like? Identified ATEs Defined with Zero-valued Outcomes are (Arbitrarily) Scale-Dependent” \nEconomists frequently estimate average treatment effects (ATEs) for transformations of the outcome that are well-defined at zero but behave like logpyq when y is large (e.g.\, logp1 ` yq\, arcsinhpyq). We show that these ATEs depend arbitrarily on the units of the outcome\, and thus should not be interpreted as percentage effects. In line with this result\, we find that estimated treatment effects for arcsinh-transformed outcomes published in the American Economic Review change substantially when we multiply the units of the outcome by 100 (e.g.\, convert dollars to cents). To help delineate alternative approaches\, we prove that when the outcome can equal zero\, there is no average treatment effect of the form EP rgpY p1q\, Y p0qqs that is point-identified and unit-invariant. We conclude by discussing sensible alternative target parameters for settings with zero-valued outcomes that relax at least one of these requirements.
URL:https://css.pre2.ss.ucla.edu/event/difference-in-difference-panel-discussion-and-mini-conference/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Conference,CCPR Seminar,CCPR Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230503T000000
DTEND;TZID=America/Los_Angeles:20230503T132000
DTSTAMP:20260423T151137
CREATED:20250424T062142Z
LAST-MODIFIED:20250424T062142Z
UID:6440-1683072000-1683120000@css.pre2.ss.ucla.edu
SUMMARY:Zack Almquist\, University of Washington
DESCRIPTION:Biography: Zack W. Almquist is currently an Associate Professor in the Department of Sociology\, Adjunct Associate Professor of Statistics\, and Senior Data Science Fellow at the eScience Institute at the University of Washington. Before coming to UW in 2020\, Prof. Almquist held positions as a Research Scientist at Facebook\, Inc and as an Assistant Professor of Sociology and Statistics at the University of Minnesota. Dr. Almquist is a recipient of the American Sociological Association’s Section on Methodology’s Leo Goodman Award. He is also a recipient of the NSF’s CAREER Award and the ARO’s Young Investigator Program Award. He is currently the Editor-in-Chief of the Journal of Mathematical Sociology. His research centers on the development and application of mathematical\, computational and statistical methodology to problems and theory of social networks\, demography\, homelessness\, and environmental action and governance. \nA Qualitative and Quantitative PIT Count using Respondent Driven Sampling (RDS): Understanding and Counting Unsheltered Homelessness in King County \nAbstract: Traditionally\, unsheltered Point in Time (PIT) Counts are the result of volunteers conducting an in-person head-count of individuals experiencing homelessness on a single night. This resource-intensive method is widely understood to be an undercount. It also fails to capture essential qualitative data about what people living unsheltered experience and need. \nThis past spring\, the King County Regional Homelessness Authority (RHA)\, in coordination with Professor Zack W. Almquist (University of Washington) and Lived Experience Coalition (LEC)\, took a novel approach to the PIT. The RHA conducted the 2022 unsheltered PIT count as a combined qualitative interview process and quantitative survey over the course of a month. The respondent selection for both the qualitative and quantitative surveys followed a Respondent Driven Sampling (RDS) protocol. RDS provides a sampling strategy for estimating size and percentages of hard-to-reach populations that lack an administrative sampling frame. \nDuring this seminar\, I will provide an overview of the RHA partnership effort\, and how we executed this novel approach to the PIT. I will review the history of RDS as a means of sampling vulnerable populations\, and I will cover the implementation of the sampling  and estimation strategies based on the RHA RDS sample. Finally\, I will review the demographics provided to HUD\, and what we learned from conducting the RDS sample for the PIT count\, and how it can and should affect future PIT counts going forwards. \nYou may access the seminar using this link.
URL:https://css.pre2.ss.ucla.edu/event/zack-almquist-university-of-washington/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230419T120000
DTEND;TZID=America/Los_Angeles:20230419T133000
DTSTAMP:20260423T151137
CREATED:20250424T061630Z
LAST-MODIFIED:20250424T061657Z
UID:6435-1681905600-1681911000@css.pre2.ss.ucla.edu
SUMMARY:Workshop: Preproducibility: what we may not\, with advantage\, omit
DESCRIPTION:Workshop: Preproducibility: what we may not\, with advantage\, omit \nPlease note that there will be no remote attendance for this event. All attendees must attend the workshop in person.  \nPanelists: Philip B. Stark (Remote)\, Yotam Shem-Tov\, Irene Bloemraad (Remote)\, and Randall Kuhn \nModerator: Patrick Heuveline \nPresenter:  \nPhilip B. Stark is Distinguished Professor of Statistics at the University of California\, Berkeley. He holds an AB in Philosophy from Princeton University and a PhD in Earth Sciences from the University of California\, San Diego. His research interests include philosophy of science and foundations of probability and statistics\, active transportation\, cosmology\, elections\, earthquakes\, gender bias\, lottery fraud\, nonparametric statistics\, physics\, regenerative agriculture\, simulation\, uncertainty quantification\, and wild food in urban ecosystems. Methods he invented for auditing elections are in law in about ten states. He has served as an expert witness or consultant for many Fortune 500 companies; the U.S. departments of Agriculture\, Commerce\, Housing and Urban Development\, Justice\, and Veterans Affairs; and numerous state agencies. He currently serves on the Board of Advisors of the U.S. Election Assistance Commission. \nWorkshop: Preproducibility: what we may not\, with advantage\, omit \nWorkshop Description: Karl Popper (1992) wrote: “Science may be described as the art of systematic oversimplification — the art of discerning what we may with advantage omit.” Communicating a scientific result requires enumerating\, recording and reporting those things that cannot with advantage be omitted. At the dawn of the Enlightenment\, chemist Robert Boyle (1660) wrote The New Experiments so “that the person I addressed them to might\, without mistake\, and with as little trouble as possible\, be able to repeat such unusual experiments.” An experiment or analysis is preproducibleif it has been described in adequate detail for others to repeat it. Most current published science is not preproducible. We need to fix that. \nModerator:  \nPatrick Heuveline is a Professor of Sociology at UCLA. He is also the Associate Director of the UCLA California Center for Population Research. \nPanelists: \nYotam Shem-Tov is an Assistant Professor of Economics at UCLA. His research primarily focuses on Labor and Public Economics with a special interest in the U.S. criminal justice system. He received a BA in Economics and Philosophy from Tel-Aviv University and a PhD in Economics from UC Berkeley. \nIrene Bloemraad (Remote) (Ph.D. Harvard; M.A. McGill) is the Class of 1951 Professor of Sociology. She also serves as the Thomas Garden Barnes Chair of Canadian Studies at Berkeley\, is the founding Director of the Berkeley Interdisciplinary Migration Initiative\, and co-directs the Boundaries\, Membership and Belonging program of the Canadian Institute for Advanced Research.In 2014-15\, she was a member of the U.S. National Academies of Sciences committee reporting on the integration of immigrants into American society. \nRandall Kuhn  is a demographer and sociologist focused on the social determinants of health among vulnerable populations. He is an expert in survey design\, longitudinal analysis and counterfactual research design. In the field of migration and health\, Kuhn has designed new approaches to estimating the impact of migration on health. In global health\, Kuhn leads a 35-year longitudinal study of the impact of health and development programs in Bangladesh. In the area of homelessness\, Kuhn conducted some of the earliest quantitative research on health and substance use risks among chronically homeless adults. He co-authored recent reports on homelessness and the coronavirus outbreak for the National Alliance to End Homelessness and on health and homelessness in Los Angeles.
URL:https://css.pre2.ss.ucla.edu/event/workshop-preproducibility-what-we-may-not-with-advantage-omit/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar,CCPR Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230201T120000
DTEND;TZID=America/Los_Angeles:20230201T132000
DTSTAMP:20260423T151137
CREATED:20250424T060756Z
LAST-MODIFIED:20250424T060756Z
UID:6431-1675252800-1675257600@css.pre2.ss.ucla.edu
SUMMARY:Graeme Blair\, UCLA
DESCRIPTION:Biography: Graeme Blair is an associate professor of political science at UCLA and serves as Co-Director of Training and Methods of Evidence in Governance and Politics (EGAP). Graeme uses experiments\, field research\, and statistics to study how to reduce violence and how to improve social science research. He works primarily in Nigeria\, often in partnership with government\, civil society\, or international organizations. His work is published in journals including Science\, Proceedings of the National Academy of Sciences\, Science Advances\, American Political Science Review\, American Journal of Political Science\, Journal of Politics\, Journal of the American Statistical Association\, and Political Analysis. His book on community policing is forthcoming with Cambridge University Press Studies in Comparative Politics and his book on research design is forthcoming with Princeton University Press. He is the recipient of the Leamer-Rosenthal Prize for Open Social Science\, the Society for Political Methodology best statistical software award\, and the Pi Sigma Alpha best paper award. \nBetter research planning through simulation\nAbstract: The talk introduces a new way of thinking about research designs in the social sciences\, with the aim of making it easier to develop and to share strong research designs. At the heart of our approach is the MIDA framework\, in which a research design is characterized by four elements: a model\, an inquiry\, a data strategy\, and an answer strategy. We have to understand each of the four on their own and also how they interrelate. The design encodes your beliefs about the world\, it describes your questions\, and it lays out how you go about answering those questions\, both in terms of what data you collect and how you analyze it. In strong designs\, choices made in the model and inquiry are reflected in the data and answer strategies\, and vice-versa. This way of thinking pays dividends at multiple points in the research design lifecycle: planning the design\, implementing it\, and integrating the results into the broader research literature. The declaration\, diagnosis\, and redesign process informs choices made from the beginning to the end of a research project. These ideas will appear in Research Design in the Social Sciences: Declaration\, Diagnosis\, and Redesign\, forthcoming in the fall with Princeton University Press. \nTo access the recording please click here.
URL:https://css.pre2.ss.ucla.edu/event/graeme-blair-ucla/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220216T120000
DTEND;TZID=America/Los_Angeles:20220216T133000
DTSTAMP:20260423T151137
CREATED:20250424T063107Z
LAST-MODIFIED:20250424T063107Z
UID:6457-1645012800-1645018200@css.pre2.ss.ucla.edu
SUMMARY:Jack Mountjoy\, University of Chicago
DESCRIPTION:Biography: Jack Mountjoy is an Assistant Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research explores the economics and econometrics of education\, labor markets\, and social mobility. Prior to joining Chicago Booth\, he was a Postdoctoral Fellow in Economics at Princeton University in the Industrial Relations Section. He is a Faculty Research Fellow at the National Bureau of Economic Research and a Research Affiliate at the University of Chicago Inclusive Economy Lab\, MIT Blueprint Labs\, and Statistics Norway. \nJack holds a Ph.D. in Economics from the University of Chicago\, where his dissertation work earned a fellowship from the National Academy of Education and Spencer Foundation. He also holds a post-baccalaureate Certificate in Mathematics from George Washington University and a B.A. in Economics and Politics from Whitman College. \n\n\nThe Returns to College(s): Relative Value-Added and Match Effects in Higher Education\n\nAbstract: Students who attend different colleges in the U.S. end up with vastly different economic outcomes. We study the role of relative value-added across colleges within student choice sets in producing these outcome disparities. Linking administrative high school records\, college applications\, admissions decisions\, enrollment spells\, degree completions\, and quarterly earnings spanning the Texas population\, we identify relative college value-added by comparing the outcomes of students who apply to and are admitted by the same set of institutions\, as this approach strikingly balances observable student potential across college treatments and renders our extensive set of covariates irrelevant as controls. Methodologically\, we develop a framework for identifying and interpreting value-added under varying assumptions about match effects and sorting gains\, generalizing the constant treatment effects assumption typically employed in the value-added literature. Empirically\, we estimate a relatively tight\, though non-degenerate\, distribution of relative value-added across the wide diversity of Texas public universities. Selectivity poorly predicts value-added within student choice sets: a fleeting selectivity earnings premium fades to zero after a few years in the labor market\, and more selective colleges tend to have lower value-added on STEM degree completion. Non-peer college inputs like instructional spending more strongly predict value-added\, especially conditional on selectivity. Educational impacts predict labor market impacts: colleges with larger earnings value-added also tend to be colleges that boost persistence\, BA completion\, and STEM degrees along the way. Finally\, we probe the potential for (mis)match effects by allowing each college’s relative value-added to vary flexibly by student characteristics. At first glance\, Black students appear to face small negative returns to choosing more selective colleges\, but this pattern of modest “mismatch” is entirely driven by the availability of two large historically Black universities with low selectivity but above-average value-added. Across the non-HBCUs\, Black students face similar returns to selectivity\, and indistinguishable value-added schedules more generally\, compared to their peers from other backgrounds.\n\nYou can access the CCPR seminar using this link.\nA recording of the seminar may be accessed here.
URL:https://css.pre2.ss.ucla.edu/event/jack-mountjoy-university-of-chicago/
LOCATION:Zoom seminar. Please contact ccpradmin@ccpr.ucla.edu for Zoom link.
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220202T120000
DTEND;TZID=America/Los_Angeles:20220202T133000
DTSTAMP:20260423T151137
CREATED:20250424T062904Z
LAST-MODIFIED:20250424T062904Z
UID:6454-1643803200-1643808600@css.pre2.ss.ucla.edu
SUMMARY:CCPR Workshop: Analyzing Sample Survey Data
DESCRIPTION:In this workshop\, attendees will learn how to analyze survey data while accounting for its complex survey design. We will demonstrate how to specify the survey design\, impute any missing data\, and analyze the survey outcomes of interest. We will discuss how our downstream “analysis” steps are related to initial operational “design” choices made by the survey data provider. We will use R but also reference equivalent Stata routines. \nslides \nThe recording may be accessed here.
URL:https://css.pre2.ss.ucla.edu/event/ccpr-workshop-analyzing-sample-survey-data/
CATEGORIES:CCPR Seminar,CCPR Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211110T120000
DTEND;TZID=America/Los_Angeles:20211110T133000
DTSTAMP:20260423T151137
CREATED:20250424T062723Z
LAST-MODIFIED:20250424T062723Z
UID:6450-1636545600-1636551000@css.pre2.ss.ucla.edu
SUMMARY:Ian Lundberg\, UCLA
DESCRIPTION:Prediction in Social Science: A Tool to Study Inequality in Populations \nBiography: Ian Lundberg is a Postdoctoral Scholar in the Department of Sociology and California Center for Population Research at UCLA. His research develops statistical and machine learning methods to answer new questions about inequality in America. Past work is published or forthcoming in PNAS\, the American Sociological Review\, Demography\, the Journal of Policy Analysis and Management\, Sociological Methodology\, Sociological Methods and Research\, and Socius. This academic year\, Ian is working on an NSF-funded postdoctoral project developing computational methods to study income mobility. In 2022\, he will begin as an Assistant Professor in the Department of Information Science at Cornell University. You can read more at ianlundberg.org. \nAbstract: Predictive algorithms could transform methodology in social science\, yet the mapping between prediction and scientific knowledge is not always clear. This talk will address three uses of prediction: (1) predicting outcomes for individual people\, (2) predicting unobserved factual outcomes to describe populations\, and (3) predicting counterfactual outcomes for causal claims. I will argue that prediction of individual-level outcomes is often difficult in social science\, yet predictive algorithms which are imperfect for individuals (1) can nonetheless be useful in support of population-level claims (2 and 3). This framework for the use of prediction is well-suited to the integration of perspectives from social science (defining the population-level quantity to be estimated) and data science (building a predictive model to estimate that quantity). \nYou can access a recording of the presentation here.
URL:https://css.pre2.ss.ucla.edu/event/ian-lundberg-ucla/
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190508T120000
DTEND;TZID=America/Los_Angeles:20190508T133000
DTSTAMP:20260423T151137
CREATED:20190426T154158Z
LAST-MODIFIED:20190426T154158Z
UID:6183-1557316800-1557322200@css.pre2.ss.ucla.edu
SUMMARY:Brandon Stewart\, Princeton University
DESCRIPTION:Title: How to Make Causal Inferences Using Texts \nAbstract: Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences\, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference\, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.  Estimating this latent representation\, however\, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks\, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences\, connecting two previously disparate literatures. (Joint Work with Egami\, Fong\, Grimmer and Roberts)
URL:https://css.pre2.ss.ucla.edu/event/brandon-stewart-princeton-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:CSS Seminar,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190501T120000
DTEND;TZID=America/Los_Angeles:20190501T133000
DTSTAMP:20260423T151137
CREATED:20190426T153944Z
LAST-MODIFIED:20190426T153944Z
UID:2053-1556712000-1556717400@css.pre2.ss.ucla.edu
SUMMARY:Susan Athey\, Stanford University
DESCRIPTION:Title: “Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies” \nAbstract: This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies\, including settings with unconfoundedness or instrumental variables.  Multi-armed bandits for learning treatment assignment policies will also be considered.
URL:https://css.pre2.ss.ucla.edu/event/susan-athey-stanford-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:CSS Seminar,Divisional Publish
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190312T140000
DTEND;TZID=America/Los_Angeles:20190312T153000
DTSTAMP:20260423T151137
CREATED:20190228T181841Z
LAST-MODIFIED:20190228T181841Z
UID:1621-1552399200-1552404600@css.pre2.ss.ucla.edu
SUMMARY:Eloise Kaizar\,  Ohio State University
DESCRIPTION:Eloise Kaizar\, Ohio State University \nRandomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants\, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently\, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk\, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators\, make evidence-based recommendations about confidence interval construction\, and present conjectures about best choices for other aspects of statistical inference. \nEloise Kaizar is Associate Professor of Statistics at The Ohio State University. Her primary research focus is on assessing the effects and safety of medical exposures and interventions\, especially those whose effects are heterogeneous across populations or measured with rare event outcomes. As such\, she has worked on methodology to combine multiple sources of information relevant to the same broad policy or patient-centered question. She is particularly interested in how data collected via different study designs can contribute complementary information.
URL:https://css.pre2.ss.ucla.edu/event/eloise-kaizar-ohio-state-university/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:CSS Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190305T140000
DTEND;TZID=America/Los_Angeles:20190305T153000
DTSTAMP:20260423T151137
CREATED:20190228T181445Z
LAST-MODIFIED:20190228T181445Z
UID:1617-1551794400-1551799800@css.pre2.ss.ucla.edu
SUMMARY:Lan Liu\, University of Minnesota at Twin Cities
DESCRIPTION:Lan Liu\, University of Minnesota at Twin Cities\n“Parsimonious Regressions for Repeated Measure Analysis” \nAbstract: Longitudinal data with repeated measures frequently arises in various\ndisciplines. The standard methods typically impose a mean outcome model as\na function of individual features\, time and their interactions. However\,\nthe validity of the estimators relies on the correct specifications of the\ntime dependency. The envelope method is recently proposed as a sufficient\ndimension reduction (SDR) method in multivariate regressions. In this\npaper\, we demonstrate the use of the envelope method as a new parsimonious\nregression method for repeated measures analysis\, where the specification\nof the underlying pattern of time trend is not required by the model. We\nfound that if there is enough prior information to support the\nspecification of the functional dependency of the mean outcome on time and\nif the dimension of the prespecified functional form is low\, then the\nstandard method is advantageous as an efficient and unbiased estimator.\nOtherwise\, the envelope method is appealing as a more robust and\npotentially efficient parsimonious regression method in repeated measure\nanalysis. We compare the performance of the envelope estimators with the\nexisting estimators in simulation study and in an application to the China\nHealth and Nutrition Survey
URL:https://css.pre2.ss.ucla.edu/event/lan-liu-university-of-minnesota-at-twin-cities/
CATEGORIES:CSS Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190226T140000
DTEND;TZID=America/Los_Angeles:20190226T153000
DTSTAMP:20260423T151137
CREATED:20190228T181503Z
LAST-MODIFIED:20190228T181503Z
UID:1616-1551189600-1551195000@css.pre2.ss.ucla.edu
SUMMARY:Adeline Lo\, Princeton University
DESCRIPTION:Adeline Lo\, Princeton University \nAbstract: High dimensional (HD) data\, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations\, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection approaches use ad hoc rules to remove noise covariates\, or select covariates through the criterion of statistical significance or by using machine learning techniques. These can suffer from lack of objectivity\, choosing some but not all predictive covariates\, and failing reasonable standards of consistency that are expected to hold in most high-dimensional social science data. The literature is scarce in statistics that can be used to directly evaluate covariate predictivity. We address these issues by proposing a variable screening step prior to traditional statistical modeling\, in which we screen covariates for their predictivity. We propose the influence (I) statistic to evaluate covariates in the screening stage\, showing that the statistic is directly related to predictivity and can help screen out noisy covariates and discover meaningful covariate interactions. We illustrate how our screening approach can removing noisy phrases from U.S. Congressional speeches and rank important ones to measure partisanship. We also show improvements to out-of-sample forecasting in a state failure application. Our approach is applicable via an open-source software package. \nAdeline Lo is a postdoctoral research associate at the Department of Politics at Princeton University. Her research lies in the design of statistical tools for prediction and measurement for applied social sciences\, with a substantive interest in conflict and post-conflict processes. She has an ongoing research agenda on high dimensional forecasting\, especially in application to violent events. Her work has been published in the Proceedings of the National Academy of Sciences\, Comparative Political Studies and Nature. She will be joining the Department of Political Science at the University of Wisconsin-Madison as an Assistant Professor in Fall 2019.
URL:https://css.pre2.ss.ucla.edu/event/adeline-lo-princeton-university-2/
CATEGORIES:CSS Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260423T151137
CREATED:20190119T195942Z
LAST-MODIFIED:20190119T195942Z
UID:1585-1548244800-1548250200@css.pre2.ss.ucla.edu
SUMMARY:Kosuke Imai\, Harvard University
DESCRIPTION:Title:  Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract:  Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai
URL:https://css.pre2.ss.ucla.edu/event/kosuke-imai-harvard-university-2/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 5201 Sumner Ave\, United States\, 101 C St\, United States\, 301 C St\, United States
CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260423T151137
CREATED:20190118T194701Z
LAST-MODIFIED:20190118T194701Z
UID:1564-1548244800-1548250200@css.pre2.ss.ucla.edu
SUMMARY:Kosuke Imai\, Harvard University
DESCRIPTION:Title:  Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract:  Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai
URL:https://css.pre2.ss.ucla.edu/event/kosuke-imai-harvard-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190117
DTEND;VALUE=DATE:20190118
DTSTAMP:20260423T151137
CREATED:20190117T212359Z
LAST-MODIFIED:20190117T212359Z
UID:1559-1547683200-1547769599@css.pre2.ss.ucla.edu
SUMMARY:Test Divisional Event
DESCRIPTION:CSS Event
URL:https://css.pre2.ss.ucla.edu/event/test-divisional-event/
CATEGORIES:Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190116T120000
DTEND;TZID=America/Los_Angeles:20190116T133000
DTSTAMP:20260423T151137
CREATED:20190118T201223Z
LAST-MODIFIED:20190118T201223Z
UID:1570-1547640000-1547645400@css.pre2.ss.ucla.edu
SUMMARY:Rocio Titiunik\, University of Michigan
DESCRIPTION:Title:  Internal vs. external validity in studies with incomplete populations \nAbstract:  Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation\, it is common to have data on all households who received the program\, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout\, information on the total number of citizens who voted is usually complete\, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology\, information on arrests by race is available\, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths\, we lack complete information about the full population of drug users. \nIn all these cases\, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study\, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However\, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens\, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions\, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference. \nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics \nMore on Prof. Titiunik
URL:https://css.pre2.ss.ucla.edu/event/rocio-titiunik-university-of-michigan/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181017T120000
DTEND;TZID=America/Los_Angeles:20181017T133000
DTSTAMP:20260423T151137
CREATED:20181003T204102Z
LAST-MODIFIED:20181003T204102Z
UID:1525-1539777600-1539783000@css.pre2.ss.ucla.edu
SUMMARY:Erin Hartman\, University of California Los Angeles
DESCRIPTION:Title: Covariate Selection for Generalizing Experimental Results\nAbstract: Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity. Although this separating set has simple mathematical representation\, it is often unclear how to select this set in applied contexts. In this paper\, we propose a data-driven method to estimate a separating set. Our approach has two advantages. First\, our algorithm relies only on the experimental data. As long as researchers can collect a rich set of covariates on experimental samples\, the proposed method can inform which variables they should adjust for. Second\, we can incorporate researcher-specific data constraints. When researchers know certain variables are unmeasurable in the target population\, our method can select a separating set subject to such constraints\, if one is feasible. We validate our proposed method using simulations\, including naturalistic simulations based on real-world data.\nCo-Sponsored with The Center for Social Statistics\nMore on Prof. Hartman
URL:https://css.pre2.ss.ucla.edu/event/erin-hartman-university-of-california-los-angeles/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish
ORGANIZER;CN="CCPR%20Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180412T120000
DTEND;TZID=America/Los_Angeles:20180412T130000
DTSTAMP:20260423T151137
CREATED:20180403T170740Z
LAST-MODIFIED:20180403T170740Z
UID:1493-1523534400-1523538000@css.pre2.ss.ucla.edu
SUMMARY:Workshop: Bayesian Concepts for Data Analysis
DESCRIPTION:Instructor: Michael Tzen \nContent:\nThis 1 hour workshop will provide a sampling of introductory concepts for bayesian analysis. We will use Bayes Rule (and its implications) to think about data analysis. When used as a framework to model phenomenon\, the analyst gets to work with 4 useful distributions: the prior\, posterior\, prior predictive\, & posterior predictive. We will predict what clothing size 2Chainz wears. We’ll also look at the Gompertz Rule from demography. In both examples\, the bayesian framework allows us to clearly express the estimand\, information from data\, information from prior knowledge\, and the estimator. \nThis workshop is the first of a two part series. The first workshop is conceptual while the second workshop will focus on software. The date for the second workshop is TBD. \nPlease RSVP Here: \nhttps://goo.gl/forms/CF4wuaobfqpug9Js1 \n 
URL:https://css.pre2.ss.ucla.edu/event/workshop-bayesian-concepts-for-data-analysis/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CSS Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180313T140000
DTEND;TZID=America/Los_Angeles:20180313T151500
DTSTAMP:20260423T151137
CREATED:20180312T165958Z
LAST-MODIFIED:20180312T165958Z
UID:1486-1520949600-1520954100@css.pre2.ss.ucla.edu
SUMMARY:Jake Bowers\, University of Illinois at Urbana-Champaign
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government\n\nCollaboration between statistical scientists (data scientists\, behavioral and social scientists\, statisticians) and policy makers promises to improve government and the lives of the public. And the data and design challenges arising from governments offer academics new chances to improve our understanding of both extant methods and behavioral and social science theory. However\, the practices that ensure the integrity of statistical work in the academy — such as transparent sharing of data and code — do not translate neatly or directly into work with governmental data and for policy ends. This paper proposes a set of practices and norms that academics and practitioners can agree on before launching a partnership so that science can advance and the public can be protected while policy can be improved. This work is at an early stage. The aim is a checklist or statement of principles or memo of understanding that can be a template for the wide variety of ways that statistical scientists collaborate with governmental actors. \n\nSpeaker:\nJake Bowers\, Associate Professor at University of Illinois and Fellow of the Office of Evaluation Sciences\nsite
URL:https://css.pre2.ss.ucla.edu/event/jake-bowers-university-of-illinois-at-urbana-champaign/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:CSS Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180221T120000
DTEND;TZID=America/Los_Angeles:20180221T133000
DTSTAMP:20260423T151137
CREATED:20180209T002136Z
LAST-MODIFIED:20180209T002136Z
UID:1413-1519214400-1519219800@css.pre2.ss.ucla.edu
SUMMARY:Yu Xie\, Princeton
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics Presents:\nHeterogeneous Causal Effects: A Propensity Score Approach\nHeterogeneity is ubiquitous in social science.  Individuals differ not only in background characteristics\, but also in how they respond to a particular treatment. In this presentation\, Yu Xie argues that a useful approach to studying heterogeneous causal effects is through the use of the propensity score. He demonstrates the use of the propensity score approach in three scenarios: when ignorability is true\, when treatment is randomly assigned\, and when ignorability is not true but there are valid instrumental variables. \nSpeaker:\nYu Xie\, Professor\, Princeton\nsite
URL:https://css.pre2.ss.ucla.edu/event/yu-xie-princeton/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CSS Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180206T140000
DTEND;TZID=America/Los_Angeles:20180206T153000
DTSTAMP:20260423T151137
CREATED:20180129T183306Z
LAST-MODIFIED:20180129T183306Z
UID:1368-1517925600-1517931000@css.pre2.ss.ucla.edu
SUMMARY:Per Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nModelling Mobility Tables as Weighted Networks\nContemporary research on occupational mobility\, i.e. how people move between jobs\, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex\, ethnicity\, age\, education or class origin and how they get access to jobs of different wages\, working conditions\, desirability\, skill profiles and job security. Consequently\, observations in occupational mobility tables are understood as independent of one another\, which allows the use of a variety of well-developed statistical models. As opposed to these “classical” approaches focussed on individual and occupational characteristics\, I am interested in modelling and understanding endogenously emerging patterns in occupational mobility tables. These emergent patterns arise from the social embedding of occupational choices\, when occupational transitions of different individuals influence each other. To analyse these emergent patterns\, I conceptualise a disaggregated mobility table as a network in which occupations are the nodes and connections are made of individuals transitioning between occupations.\n\n\nIn this paper\, I present a statistical model to analyse these weighted mobility networks. The approach to modelling mobility as an interdependent system is inspired by the exponential random graph model (ERGM); however\, some differences arise from ties being weighted as well as from specific constraints of mobility tables. The model is applied to data on intra-generational mobility to analyse the interdependent transitions of men and women through the labour market\, as well as to understanding the extent to which clustering in mobility can be modelled by exogenously defined social classes or through endogenous structures.\n  \nPer Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)\nsite
URL:https://css.pre2.ss.ucla.edu/event/per-block-eth-zurich/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:CSS Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180124T120000
DTEND;TZID=America/Los_Angeles:20180124T133000
DTSTAMP:20260423T151137
CREATED:20180119T224032Z
LAST-MODIFIED:20180119T224032Z
UID:1361-1516795200-1516800600@css.pre2.ss.ucla.edu
SUMMARY:Rob Warren\, University of Minnesota
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics presents:\nWhen Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations?\nMany researchers uncritically use inferential statistical procedures (e.g.\, hypothesis tests) when analyzing complete population data—a situation in which inference may seem unnecessary. We begin by reviewing and analyzing the most common rationales for employing inferential procedures when analyzing full population data. Two common rationales—having to do with handling missing data and generalizing results to other times and/or places—either lack merit or amount to analyzing sample (not population) data.  Whether it is appropriate to use inferential procedures depends on whether researchers are analyzing sample or population data and on whether they seek to make causal or descriptive claims. When doing descriptive research\, the distinction between sample and population data is paramount: Inferential statistics should only be used to analyze sample data (to account for sampling variability) and never to analyze population data. When doing causal research\, the distinction between sample data and population data is unimportant: Inferential procedures can and should always be used to distinguish (for example) robust associations from those that may have come about by chance alone. Crucially\, using inferential procedures to analyze population data to make descriptive claims can lead to incorrect substantive conclusions—especially when population sizes and/or effect sizes are small. \nSpeaker:\nRob Warren\, Professor of Sociology and Director of the Minnesota Population Center\nsite
URL:https://css.pre2.ss.ucla.edu/event/rob-warren-university-minnesota/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CSS Seminar
END:VEVENT
END:VCALENDAR