Abstract:
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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.
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