题目：Improving Robustness of the Model-X Inference with Application to EHR Studies

报告人：刘默雷 研究员（北京大学）

地点：致远楼101室

时间：2025年11月25日 10：00-11：30

摘要：The model-X conditional randomization test (CRT) is a flexible and powerful testing procedure for conditional independence testing. However, it requires perfect knowledge of the exposure X’s conditional distribution and may lose its validity when there is an error in modeling X. This problem is even more severe when the adjustment covariates Z are high-dimensional. To address this challenge, we propose the Maxway CRT, which learns the conditional distribution of the response Y and uses it to calibrate the resampling distribution of X. We prove that the type-I error inflation of the Maxway CRT can be controlled by the learning error for a low-dimensional adjusting model plus the product of learning errors for X | Z and Y | Z, interpreted as an “almost doubly robust” property. Based on this, we develop implementing algorithms of the Maxway CRT in practical scenarios including surrogate-assisted semi-supervised learning and transfer learning. We apply our methodology to two real-world studies on electronic health record and biobank data.

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