Statistical and Causal Robustness for Causal Null Hypothesis Tests
因果零假设检验的统计稳健性与因果稳健性AI 解读 ↓
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因果零假设检验的统计稳健性与因果稳健性
- 研究问题
- 当因果识别模型可能错设时，如何组合多个候选模型对应的检验，使共同因果零假设的检验兼具统计与因果稳健性？
- 方法与思路
- 作者结合半参数理论与证据因子，利用不同识别泛函对应的多个渐近线性估计量的联合渐近正态性，构造零假设检验。
- 材料陈述的贡献
- 摘要称，在相应条件下，只要至少一个候选因果模型正确，检验即可有效，同时允许干扰函数估计以慢于参数速率收敛。
- 阅读关注点
- 应核对共同零假设的定义、多个估计量的联合分布和检验组合规则，以及至少一个模型正确时仍须满足的统计条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出完整速率与有效性条件，也未声称所有候选模型都错时仍有效。
原文摘要
Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumptions. Evidence factors is an approach to combining hypothesis tests of a common causal null hypothesis under two or more candidate causal models. Under certain conditions, this yields a test that is valid if at least one of the underlying models is correct, which is a form of causal robustness. We propose a method of combining semiparametric theory with evidence factors. We develop a causal null hypothesis test based on joint asymptotic normality of K asymptotically linear semiparametric estimators, where each estimator is based on a distinct identifying functional derived from each of K candidate causal models. We show that this test provides both statistical and causal robustness in the sense that it is valid if at least one of the K proposed causal models is correct, while also allowing for slower than parametric rates of convergence in estimating nuisance functions. We demonstrate the effectiveness of our method via simulations and applications to the Framingham Heart Study and Wisconsin Longitudinal Study.