BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UCLA Center for Social Statistics - ECPv6.15.17.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://css.pre2.ss.ucla.edu X-WR-CALDESC:Events for UCLA Center for Social Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20170312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20171105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20180311T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20181104T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20190310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20191103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20200308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20201101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20210314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20211107T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20220313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20221106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240522T130000 DTEND;TZID=America/Los_Angeles:20240522T160000 DTSTAMP:20260610T203921 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:20260610T203921 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:20231101T120000 DTEND;TZID=America/Los_Angeles:20231101T131500 DTSTAMP:20260610T203921 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:20260610T203921 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:20190508T120000 DTEND;TZID=America/Los_Angeles:20190508T133000 DTSTAMP:20260610T203921 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:20260610T203921 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:20190123T120000 DTEND;TZID=America/Los_Angeles:20190123T133000 DTSTAMP:20260610T203921 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:20260610T203921 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:20260610T203921 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:20181017T120000 DTEND;TZID=America/Los_Angeles:20181017T133000 DTSTAMP:20260610T203921 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 END:VCALENDAR