Treatment bootstrapping: A new approach to quantify uncertainty of average treatment effect estimates
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914917100027904 |
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| author | Li, Jing |
| author_facet | Li, Jing |
| contents | This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimate for the treated from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart control group by pair matching on estimated propensity score without replacement. We demonstrate the validity of this approach and compare it with existing bootstrap approaches through Monte Carlo simulation and analysis of a real world data set. The results indicate that the proposed approach constructs confidence intervals and standard errors that have 95 percent or above coverage rate and better precision compared with existing bootstrap approaches, while these measures also depend on percent treated in the sample data and the sample size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11683 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Treatment bootstrapping: A new approach to quantify uncertainty of average treatment effect estimates Li, Jing Methodology Applications This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimate for the treated from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart control group by pair matching on estimated propensity score without replacement. We demonstrate the validity of this approach and compare it with existing bootstrap approaches through Monte Carlo simulation and analysis of a real world data set. The results indicate that the proposed approach constructs confidence intervals and standard errors that have 95 percent or above coverage rate and better precision compared with existing bootstrap approaches, while these measures also depend on percent treated in the sample data and the sample size. |
| title | Treatment bootstrapping: A new approach to quantify uncertainty of average treatment effect estimates |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2310.11683 |