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Activity Number: 633 - Model-Based Statistics and Applications
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Government Statistics Section
Abstract #327297
Title: Finding and Combining Multiple Designed Data Sets for Estimating Causal Effects in Observational Studies
Author(s): Zach Branson* and Marie-Abele Bind
Companies: Harvard University and Harvard University
Keywords: Causal inference; observational studies; matching; covariate balance; randomization inference; assignment mechanism
Abstract:

Many causal inference methods for observational studies focus on a subset of units that exhibit covariate balance among treatment groups and thus plausibly reconstruct a hypothetical randomized experiment. For example, matching methodologies match units that are similar with respect to background covariates, and regression discontinuity designs restrict analyses to units around the discontinuity with covariate balance. For these methods, it is common to (1) restrict analyses to a single subset, and (2) analyze that subset as if it were from a completely randomized experiment, regardless of the level of covariate balance in that subset. Instead, we propose a method that (1) finds multiple subsets that plausibly reconstruct a hypothetical randomized experiment, (2) analyzes each subset conditional on the level of covariate balance that subset achieves, and (3) combines these analyses into a single point estimate and uncertainty interval. Using an empirical example, we show that our procedure yields more precise inferences than standard methodologies by utilizing more units in the study as well as taking advantage of assignment mechanisms that account for covariate balance.


Authors who are presenting talks have a * after their name.

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