Reconciling model-X and doubly robust approaches to conditional independence testing
协调 model-X 与双重稳健条件独立检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究条件独立检验而未给出因果目标或识别设计，不能仅凭双重稳健术语归为因果推断。
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协调 model-X 与双重稳健条件独立检验
- 研究问题
- 当 model-X 检验使用样本内学习的预测变量条件分布时，何时仍能控制第一类错误，其与双重稳健检验有何联系？
- 方法与思路
- 作者分析蒸馏条件随机化检验 dCRT，与广义协方差度量检验 GCM 比较，并以半参数效率理论研究特定备择。
- 材料陈述的贡献
- 摘要称 dCRT 具有双重稳健性，两种检验渐近等价，且 GCM 对广义部分线性备择最优；模拟还比较了有限样本表现。
- 阅读关注点
- 应核对条件均值估计所需精度、双重稳健性的准确条件、渐近等价范围，以及 post-lasso 改善错误控制的设置。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；未列出估计误差条件，最优性限于所述备择，条件独立检验本身不能确立因果关系。
原文摘要
Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional distribution of the predictor given the covariates. Nevertheless, model-X methodologies are often deployed with this conditional distribution learned in sample. We investigate the consequences of this choice through the lens of the distilled conditional randomization test (dCRT). We find that Type-I error control is still possible, but only if the mean of the outcome variable given the covariates is estimated well enough. This demonstrates that the dCRT is doubly robust, and motivates a comparison to the generalized covariance measure (GCM) test, another doubly robust conditional independence test. We prove that these two tests are asymptotically equivalent, and show that the GCM test is optimal against (generalized) partially linear alternatives by leveraging semiparametric efficiency theory. In an extensive simulation study, we compare the dCRT to the GCM test. These two tests have broadly similar Type-I error and power, though dCRT can have somewhat better Type-I error control but somewhat worse power in small samples or when the response is discrete. We also find that post-lasso based test statistics (as compared to lasso based statistics) can dramatically improve Type-I error control for both methods.