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Erin Hartman, University of California Los Angeles

CCPR Seminar Room, 4240 Public Affairs Building, Los Angeles, CA, 90095, United States 101 Sumner Ave, United States

Title: Covariate Selection for Generalizing Experimental Results Abstract: 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 […]

Rocio Titiunik, University of Michigan

4240 Public Affairs Building 4240 Public Affairs Building, Los Angeles, CA, United States

Internal vs. external validity in studies with incomplete populations

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.

In 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.

Co-sponsored with the Political Science Department, Statistics Department and the Center for Social Statistics 

Kosuke Imai, Harvard University

CCPR Seminar Room, 4240 Public Affairs Building, Los Angeles, CA, 90095, United States 101 Sumner Ave, United States

Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data Abstract: 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 […]

Kosuke Imai, Harvard University

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

Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data Abstract: 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 […]

Ian Lundberg, UCLA

Prediction in Social Science: A Tool to Study Inequality in Populations Biography: 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 […]

CCPR Workshop: Analyzing Sample Survey Data

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 […]

Jack Mountjoy, University of Chicago

Zoom seminar. Please contact ccpradmin@ccpr.ucla.edu for Zoom link.

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 […]

Graeme Blair, UCLA

4240A Public Affairs Bldg

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 […]

Workshop: Preproducibility: what we may not, with advantage, omit

4240A Public Affairs Bldg

Workshop: Preproducibility: what we may not, with advantage, omit Please note that there will be no remote attendance for this event. All attendees must attend the workshop in person.  Panelists: Philip B. Stark (Remote), Yotam Shem-Tov, Irene Bloemraad (Remote), and Randall Kuhn Moderator: Patrick Heuveline Presenter:  Philip B. Stark is Distinguished Professor of Statistics at the University of […]