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X-WR-CALDESC:Events for UCLA Center for Social Statistics
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DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260514T174931
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:20260514T174931
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;TZID=America/Los_Angeles:20190116T120000
DTEND;TZID=America/Los_Angeles:20190116T133000
DTSTAMP:20260514T174931
CREATED:20190118T201223Z
LAST-MODIFIED:20190118T201223Z
UID:1570-1547640000-1547645400@css.stat.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.stat.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:20260514T174931
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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