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DTSTART;TZID=America/Los_Angeles:20190116T120000
DTEND;TZID=America/Los_Angeles:20190116T133000
DTSTAMP:20260723T230323
CREATED:20180828T011011Z
LAST-MODIFIED:20190118T194235Z
UID:10000633-1547640000-1547645400@ccpr.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://ccpr.ucla.edu/event/rocio-titiunik-university-of-michigan/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Titiunik_Rocio.jpg
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DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260723T230323
CREATED:20180828T011625Z
LAST-MODIFIED:20190118T194253Z
UID:10000634-1548244800-1548250200@ccpr.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://ccpr.ucla.edu/event/kosuke-imai-harvard/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/png:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Iami_Kosuke.png
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DTSTART;TZID=America/Los_Angeles:20190130T120000
DTEND;TZID=America/Los_Angeles:20190130T133000
DTSTAMP:20260723T230323
CREATED:20180828T012035Z
LAST-MODIFIED:20190107T173339Z
UID:10000635-1548849600-1548855000@ccpr.ucla.edu
SUMMARY:Ushma Upadhyay\, UC San Francisco
DESCRIPTION:Title:  Measuring Reproductive Autonomy: Are the questions different for Adolescents and Young Adults? \nAbstract:  Ushma Upadhyay\, PhD\, MPH will discuss her previous work developing the Reproductive Autonomy Scale\, which has been mainly used among adults.  Her current work focuses on understanding reproductive empowerment among young people\, and the development of a new psychometric measure of Sexual Health and Reproductive Empowerment for Young Adults (The SHREYA Scale).     \nCo-sponsored with The Bixby Center \nMore on Prof. Upadhyay
URL:https://ccpr.ucla.edu/event/ushma-upadhyay-uc-san-francisco/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Upadhyay_Ushma.jpeg
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