Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
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保持可忽略性并实现协变量匹配的处理效应表示学习
- 研究问题
- 观测数据同时存在隐藏混杂和处理组、对照组协变量不匹配时，如何学习兼具有效调整与分布匹配性质的表示？
- 方法与思路
- 作者先利用带因果辅助信息的锚变量构造领域并进行梯度匹配，再施加协变量匹配变换，组合两类神经网络架构。
- 材料陈述的贡献
- 摘要称近似不变表示可产生近似有效调整并给出真实因果效应附近的区间，且在多个基准上取得较低 ATE 与 PEHE 误差。
- 阅读关注点
- 应核对锚变量与辅助因果信息的要求、可检验近似如何转化为效应界，以及匹配变换是否保留有效调整性质。
- 阅读边界
- 依据 arXiv 预印本摘要，同行评审及期刊接收未核实；摘要未给出近似误差界和识别条件，基准优势不能保证消除任意隐藏混杂。
原文摘要
Estimating treatment effects from observational data is challenging due to two main reasons: (a) hidden confounding, and (b) covariate mismatch (control and treatment groups not having identical distributions). Long lines of works exist that address only either of these issues. To address the former, conventional techniques that require detailed knowledge in the form of causal graphs have been proposed. For the latter, covariate matching and importance weighting methods have been used. Recently, there has been progress in combining testable independencies with partial side information for tackling hidden confounding. A common framework to address both hidden confounding and selection bias is missing. We propose neural architectures that aim to learn a representation of pre-treatment covariates that is a valid adjustment and also satisfies covariate matching constraints. We combine two different neural architectures: one based on gradient matching across domains created by subsampling a suitable anchor variable that assumes causal side information, followed by the other, a covariate matching transformation. We prove that approximately invariant representations yield approximate valid adjustment sets which would enable an interval around the true causal effect. In contrast to usual sensitivity analysis, where an unknown nuisance parameter is varied, we have a testable approximation yielding a bound on the effect estimate. We also outperform various baselines with respect to ATE and PEHE errors on causal benchmarks that include IHDP, Jobs, Cattaneo, and an image-based Crowd Management dataset.