Estimating population average treatment effects from experiments with noncompliance

Ottoboni Kellie N. and Poulos Jason V. ()
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Ottoboni Kellie N.: Department of Statistics, University of California, Berkeley, California, 94720, United States of America
Poulos Jason V.: Department of Statistical Science, Duke University and SAMSI, Durham, North Carolina, 27708, United States of America

Journal of Causal Inference, 2020, vol. 8, issue 1, 108-130

Abstract: Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart when compliance is relatively low and can be predicted by observed covariates. We apply the method to evaluate the effect of Medicaid coverage on health care use for a target population of adults who may benefit from expansions to the Medicaid program. We draw RCT data from the Oregon Health Insurance Experiment, where less than one-third of those randomly selected to receive Medicaid benefits actually enrolled.

Keywords: Causal inference; external validity; health insurance; observational studies; noncompliance; randomized controlled trials (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:8:y:2020:i:1:p:108-130:n:3

DOI: 10.1515/jci-2018-0035

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