Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes
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通过量化对处理效应估计的影响改进偏差校正评价标准
- 研究问题
- 不同倾向得分匹配方法即使通过常规检查，也可能产生不同处理效应估计；如何增加评价依据以减少方法选择的任意性？
- 方法与思路
- 作者提出 A2A 指标，通过构造与原任务相似且结果已知的人工匹配任务，评价从倾向估计到处理效应估计的完整流程。
- 材料陈述的贡献
- 摘要称，A2A 与标准化均值差联合使用可降低所测任务的效应估计误差和估计变异，并提供整合匹配方法的 popmatch 工具。
- 阅读关注点
- 应核对人工任务的已知结果如何构造、与原任务的可比性，以及评价指标改善是否对应真实偏差下降和有效因果识别。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。匹配和指标通过不能自动保证因果识别；摘要未说明未观测混杂的处理或适用条件。
原文摘要
With the growing access to administrative health databases, retrospective studies have become crucial evidence for medical treatments. Yet, non-randomized studies frequently face selection biases, requiring mitigation strategies. Propensity score matching (PSM) addresses these biases by selecting comparable populations, allowing for analysis without further methodological constraints. However, PSM has several drawbacks. Different matching methods can produce significantly different Average Treatment Effects (ATE) for the same task, even when meeting all validation criteria. To prevent cherry-picking the best method, public authorities must involve field experts and engage in extensive discussions with researchers. To address this issue, we introduce a novel metric, A2A, to reduce the number of valid matches. A2A constructs artificial matching tasks that mirror the original ones but with known outcomes, assessing each matching method's performance comprehensively from propensity estimation to ATE estimation. When combined with Standardized Mean Difference, A2A enhances the precision of model selection, resulting in a reduction of up to 50% in ATE estimation errors across synthetic tasks and up to 90% in predicted ATE variability across both synthetic and real-world datasets. To our knowledge, A2A is the first metric capable of evaluating outcome correction accuracy using covariates not involved in selection. Computing A2A requires solving hundreds of PSMs, we therefore automate all manual steps of the PSM pipeline. We integrate PSM methods from Python and R, our automated pipeline, a new metric, and reproducible experiments into popmatch, our new Python package, to enhance reproducibility and accessibility to bias correction methods.