# Estimating population average treatment effects from experiments with noncompliance

## TL;DR

This paper introduces a reweighting method to accurately estimate population average treatment effects from RCTs with noncompliance, improving estimates especially when compliance is low and predictable.

## Contribution

It proposes a novel compliance-adjusted estimator that accounts for noncompliance in RCTs to better reflect population effects.

## Key findings

- The estimator outperforms unadjusted methods in simulations with low compliance.
- It effectively predicts compliance using observed covariates.
- Applied to Medicaid data, it provides more accurate population effect estimates.

## 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.