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Estimating population average treatment effects from experiments with noncompliance
| Content Provider | Scilit |
|---|---|
| Author | Ottoboni, Kellie N. Poulos, Jason V. |
| Copyright Year | 2020 |
| 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. |
| Related Links | https://www.degruyter.com/downloadpdf/journals/jci/8/1/article-p108.pdf |
| Ending Page | 130 |
| Page Count | 23 |
| Starting Page | 108 |
| ISSN | 21933677 |
| e-ISSN | 21933685 |
| DOI | 10.1515/jci-2018-0035 |
| Journal | Journal of Causal Inference |
| Issue Number | 1 |
| Volume Number | 8 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2020-10-23 |
| Access Restriction | Open |
| Subject Keyword | Journal of Causal Inference Statistics and Probability Causal Inference External Validity Health Insurance Observational Studies Noncompliance Randomized Controlled Trials 62d20 62p20 62p25 Journal: Journal of Causal Inference, Issue- 3 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
