RCT Rejection Sampling for Causal Estimation Evaluation
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用于因果估计评估的随机对照试验拒绝采样
- 研究问题
- 如何从随机对照试验构造含混杂的观察样本，同时保留与试验平均因果效应的有效可比性，以评估混杂调整方法？
- 方法与思路
- 作者提出 RCT 拒绝采样算法并给出识别保证，以模拟检查理想估计器的偏差，再用含高维文本协变量的真实试验展示评估流程。
- 材料陈述的贡献
- 摘要称，新算法支持有效的试验基准比较，并讨论有限数据下的设计问题，公开约七万条观测的试验数据作为实例。
- 阅读关注点
- 应核对采样机制与识别证明、采样后目标人群和效应是否变化，以及试验基准估计误差与有效样本量如何影响评估。
- 阅读边界
- 这是预印本，同行评审或接收未经核实。摘要未说明理论保证的具体条件、采样接受率或试验基准的不确定性处理。
原文摘要
Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have proposed methods to adjust for confounding by adapting machine learning methods to the goal of causal estimation. However, empirical evaluation of these adjustment methods has been challenging and limited. In this work, we build on a promising empirical evaluation strategy that simplifies evaluation design and uses real data: subsampling randomized controlled trials (RCTs) to create confounded observational datasets while using the average causal effects from the RCTs as ground-truth. We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT. Using synthetic data, we show our algorithm indeed results in low bias when oracle estimators are evaluated on the confounded samples, which is not always the case for a previously proposed algorithm. In addition to this identification result, we highlight several finite data considerations for evaluation designers who plan to use RCT rejection sampling on their own datasets. As a proof of concept, we implement an example evaluation pipeline and walk through these finite data considerations with a novel, real-world RCT -- which we release publicly -- consisting of approximately 70k observations and text data as high-dimensional covariates. Together, these contributions build towards a broader agenda of improved empirical evaluation for causal estimation.