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Rob Warren, University of Minnesota

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

The California Center for Population Research and the Center for Social Statistics presents: When Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations? Many researchers uncritically use inferential statistical procedures (e.g., hypothesis tests) when analyzing complete population data—a situation in which inference may seem unnecessary. We begin by reviewing and analyzing the most […]

Per Block, ETH Zurich (Swiss Federal Institute of Technology in Zurich)

Franz Hall 2258A Franz Hall 2258A

The UCLA Department of Statistics and the Center for Social Statistics presents: Modelling Mobility Tables as Weighted Networks Contemporary research on occupational mobility, i.e. how people move between jobs, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex, ethnicity, age, education or class origin and […]

Yu Xie, Princeton

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

The California Center for Population Research and the Center for Social Statistics Presents: Heterogeneous Causal Effects: A Propensity Score Approach Heterogeneity is ubiquitous in social science.  Individuals differ not only in background characteristics, but also in how they respond to a particular treatment. In this presentation, Yu Xie argues that a useful approach to studying […]

Jake Bowers, University of Illinois at Urbana-Champaign

Franz Hall 2258A Franz Hall 2258A

The UCLA Department of Statistics and the Center for Social Statistics presents: Rules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government Collaboration between statistical scientists (data scientists, behavioral and social scientists, statisticians) and policy makers promises to improve government and the lives of the public. And the data and design […]

Workshop: Bayesian Concepts for Data Analysis

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

Instructor: Michael Tzen Content: This 1 hour workshop will provide a sampling of introductory concepts for bayesian analysis. We will use Bayes Rule (and its implications) to think about data analysis. When used as a framework to model phenomenon, the analyst gets to work with 4 useful distributions: the prior, posterior, prior predictive, & posterior […]

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 […]