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DTSTART;TZID=America/Los_Angeles:20230419T120000
DTEND;TZID=America/Los_Angeles:20230419T133000
DTSTAMP:20260610T223055
CREATED:20250424T061630Z
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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:20260610T223055
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:20260610T223055
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:20260610T223055
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:20260610T223055
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:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260610T223055
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:20260610T223055
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;TZID=America/Los_Angeles:20190116T120000
DTEND;TZID=America/Los_Angeles:20190116T133000
DTSTAMP:20260610T223055
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:20260610T223055
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
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