Conditional average treatment effect estimation with marginally constrained models
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使用边际约束模型估计条件平均处理效应
- 研究问题
- 如何结合随机试验报告的边际优势比与大型观察数据估计个体化处理效应，并避免将边际效应误当成条件效应？
- 方法与思路
- 作者为条件平均处理效应模型引入边际约束或正则项，比较其与优势比偏置项方法，并考察未观测混杂下的近似表现。
- 材料陈述的贡献
- 摘要称，所提约束在标准观察性因果推断假设下给出一致估计；存在未观测混杂时，偏置项方法一般不能有效估计条件效应。
- 阅读关注点
- 应核对试验边际优势比如何进入约束、试验与观察样本如何衔接，以及一致性结论和混杂下近似比较分别依赖哪些条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未展开标准假设，也未给出效果异质性阈值或误差改善数值。
原文摘要
Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population. Estimates of the conditional average treatment effect (CATE) are more useful for individualized treatment decision making, but randomized trials are often too small to estimate the CATE. Examples in medical literature make use of the relative treatment effect (e.g. an odds-ratio) reported by randomized trials to estimate the CATE using large observational datasets. One approach to estimating these CATE models is by using the relative treatment effect as an offset, while estimating the covariate-specific untreated risk. We observe that the odds-ratios reported in randomized controlled trials are not the odds-ratios that are needed in offset models because trials often report the marginal odds-ratio. We introduce a constraint or regularizer to better use marginal odds-ratios from randomized controlled trials and find that under the standard observational causal inference assumptions this approach provides a consistent estimate of the CATE. Next, we show that the offset approach is not valid for CATE estimation in the presence of unobserved confounding. We study if the offset assumption and the marginal constraint lead to better approximations of the CATE relative to the alternative of using the average treatment effect estimate from the randomized trial. We empirically show that when the underlying CATE has sufficient variation, the constraint and offset approaches lead to closer approximations to the CATE.