Abstract
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.
Highlights
Randomized control trials (RCTs) are the gold standard for estimating the causal effect of a treatment
Kowalski [30] re-weights the Local Average Treatment Effect (LATE) estimated on the Oregon Health Insurance Experiment (OHIE) sample to a broader population in Massachusetts using observational data from the Behavioral Risk Factor Surveillance System (BRFSS) [31] and finds Medicaid significantly decreased the probability of visiting the emergency room (ER), and had no significant effect on the number of ER visits
We acquire data on the target population from the National Health Interview Study (NHIS) [32] for the period 2008 to 2017.6 We restrict the sample to respondents with income below 138% of the Federal Poverty Level (FPL) and who are uninsured or on Medicaid and select covariates on respondent characteristics that match the OHIE pretreatment covariates
Summary
Randomized control trials (RCTs) are the gold standard for estimating the causal effect of a treatment. We propose a reweighting method for estimating complier–average causal effects for the target population from RCT data with noncompliance, and refer to this estimator as the Population Average Treatment Effect on Treated Compliers (PATT-C). Our approach for estimating PATT-C differs from previous reweighting methods because we only need to estimate the potential outcomes for RCT compliers and we cannot observe who in the control group would have complied had they been assigned treatment. When estimating the average causal effect for compliers from an RCT, researchers typically scale the estimated ITT effect by the compliance rate, assuming that there is only single crossover from treatment to control.. The paper proceeds as follows: Section 2 presents the proposed estimator and the necessary assumptions for its identifiability; Section 3 describes the estimation procedure; Section 4 reports the estimator’s performance in simulations; Section 5 uses the estimator to identify the effect of extending Medicaid coverage to the low–income adult population in the U.S.; Section 6 discusses the results and offers direction for future research
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