Estimating population average treatment effects from experiments with noncompliance
| Autor: | Jason Poulos, Kellie Ottoboni |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences 62d20 Average treatment effect Population Econometrics (econ.EM) 01 natural sciences Statistics - Applications QA273-280 law.invention External validity Methodology (stat.ME) FOS: Economics and business 010104 statistics & probability Randomized controlled trial law 0103 physical sciences Health care Statistics external validity QA1-939 Medicine Applications (stat.AP) causal inference 0101 mathematics 010306 general physics education observational studies Statistics - Methodology Economics - Econometrics education.field_of_study business.industry 62p25 Causal inference health insurance randomized controlled trials 62p20 Observational study noncompliance Statistics Probability and Uncertainty business Probabilities. Mathematical statistics Medicaid Mathematics |
| Zdroj: | Journal of Causal Inference, Vol 8, Iss 1, Pp 108-130 (2020) |
| DOI: | 10.48550/arxiv.1901.02991 |
| Popis: | 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. Comment: Forthcoming, Journal of Causal Inference |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|