Hi all,
Our next virtual meeting will be Wednesday April 22, where we will hear
Erin Hartman present joint research with Naoki Egami on "*Covariate
Selection for Generalizing Experimental Results: Application to Large-Scale
Development Program in Uganda*”.
*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 paper can be found here <https://arxiv.org/abs/1909.02669>.
*Zoom link*:
https://harvard.zoom.us/j/987462892
*When*: Wednesday, April 22 at 12noon - 1:30pm.
Best,
Georgie