Today's speaker: Erin Hartman, "Covariate Selection for Generalizing Experimental Results: Application to Large-Scale Development Program in Uganda” (joint research with Naoki Egami)
Abstract: Generalizing estimates of causal effects from an experiment to a target population is of interest to scientists. However, researchers are usually constrained by available covariate information. Analysts can often collect much fewer variables from population samples than from experimental samples, which has limited applicability of existing approaches that assume rich covariate data from both experimental and population samples. In this article, we examine how to select covariates necessary for generalizing experimental results under such data constraints. In our concrete context of a large-scale development program in Uganda, although more than 40 pre-treatment covariates are available in the experiment, only 8 of them were also measured in a target population. We propose a method to estimate a separating set -- a set of variables affecting both the sampling mechanism and treatment effect heterogeneity -- and show that the population average treatment effect (PATE) can be identified by adjusting for estimated separating sets. Our algorithm only requires a rich set of covariates in the experimental data, not in the target population, by incorporating researcher-specific constraints on what variables are measured in the population data. Analyzing the development experiment in Uganda, we show that the proposed algorithm can allow for the PATE estimation in situations where conventional methods fail due to data requirements.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. Free lunch is provided.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009