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X-ORIGINAL-URL:https://css.stat.ucla.edu
X-WR-CALDESC:Events for UCLA Center for Social Statistics
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DTSTART;TZID=America/Los_Angeles:20190508T120000
DTEND;TZID=America/Los_Angeles:20190508T133000
DTSTAMP:20260430T105227
CREATED:20190426T154158Z
LAST-MODIFIED:20190426T154158Z
UID:2058-1557316800-1557322200@css.stat.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.stat.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:20260430T105227
CREATED:20190426T153944Z
LAST-MODIFIED:20190426T153944Z
UID:2053-1556712000-1556717400@css.stat.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.stat.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=image/jpeg:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/04/susan-athey.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260430T105227
CREATED:20190119T195942Z
LAST-MODIFIED:20190122T214513Z
UID:1585-1548244800-1548250200@css.stat.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.stat.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=image/png:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/01/Iami_Kosuke-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260430T105227
CREATED:20190118T194701Z
LAST-MODIFIED:20190122T214512Z
UID:1564-1548244800-1548250200@css.stat.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.stat.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=image/png:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/01/Iami_Kosuke-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190117
DTEND;VALUE=DATE:20190118
DTSTAMP:20260430T105227
CREATED:20190117T212359Z
LAST-MODIFIED:20190117T212359Z
UID:1559-1547683200-1547769599@css.stat.ucla.edu
SUMMARY:Test Divisional Event
DESCRIPTION:CSS Event
URL:https://css.stat.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:20260430T105227
CREATED:20181003T204102Z
LAST-MODIFIED:20181017T172052Z
UID:1525-1539777600-1539783000@css.stat.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.stat.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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