Bias Mitigation in Matched Observational Studies with Continuous Treatments: Calipered Non-Bipartite Matching and Bias-Corrected Estimation and Inference
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连续处理匹配观察研究的偏差缓解：卡尺非二分匹配与偏差修正推断
- 研究问题
- 在连续处理的观察研究中，协变量匹配不精确会如何影响因果估计，又能否在匹配、估计与推断各阶段减轻偏差？
- 方法与思路
- 作者设计同时利用协变量与处理剂量的匹配卡尺，并提出偏差修正的 Neyman 估计量及配套的偏差修正方差估计量。
- 材料陈述的贡献
- 摘要称常用匹配后平衡标准仍可能留下严重偏差，并以模拟及社交距离研究重分析展示所提框架，另提供 R 包。
- 阅读关注点
- 应核对卡尺的构造与匹配目标、偏差修正依赖的模型条件，以及方差估计是否充分反映匹配过程和修正项的不确定性。
- 阅读边界
- 这是 arXiv 预印本摘要，不能据此认定同行评审或期刊接收；摘要未说明因果识别条件、偏差幅度及数值比较的具体设置。
原文摘要
In matched observational studies with continuous treatments, individuals with different treatment doses but the same or similar covariate values are paired for causal inference. While inexact covariate matching (i.e., covariate imbalance after matching) is common in practice, previous matched studies with continuous treatments have often overlooked this issue as long as post-matching covariate balance meets certain criteria. Through re-analyzing a matched observational study on the social distancing effect on COVID-19 case counts, we show that this routine practice can introduce severe bias for causal inference. Motivated by this finding, we propose a general framework for mitigating bias due to inexact matching in matched observational studies with continuous treatments, covering the matching, estimation, and inference stages. In the matching stage, we propose a carefully designed caliper that incorporates both covariate and treatment dose information to improve matching for downstream treatment effect estimation and inference. For the estimation and inference, we introduce a bias-corrected Neyman estimator paired with a corresponding bias-corrected variance estimator. The effectiveness of our proposed framework is demonstrated through numerical studies and a re-analysis of the aforementioned observational study on the effect of social distancing on COVID-19 case counts. An open-source R package for implementing our framework has also been developed.