Doubly Robust Criterion for Causal Inference
因果推断中的双重稳健模型选择准则AI 解读 ↓
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因果推断中的双重稳健模型选择准则
- 研究问题
- 在倾向得分与结果模型共同参与的因果分析中，如何选择回归结构，使模型选择准则在其中一个模型误设时仍能有效估计风险？
- 方法与思路
- 作者以 Kullback–Leibler 散度定义风险，依据 AIC 的原始思路推导适用于一般因果模型的准则，并使该准则具有双重稳健性。
- 材料陈述的贡献
- 摘要称，只要分配模型或结果模型之一正确，准则仍渐近无偏地估计风险；所列模拟中，它比既有准则更常选中真实或近真实模型。
- 阅读关注点
- 应核对风险函数对应的目标总体、双重稳健性所需条件、偏差修正项，以及风险估计有效性与选中真实模型之间的区别。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核验；摘要未给出完整假设，模拟优势不意味着所有模型空间中的选择一致性或普遍优越性。
原文摘要
The semiparametric estimation approach, which includes inverse-probability-weighted and doubly robust estimation using propensity scores, is a standard tool in causal inference, and it is rapidly being extended in various directions. On the other hand, although model selection is indispensable in statistical analysis, an information criterion for selecting an appropriate regression structure has just started to be developed. In this paper, based on the original definition of Akaike information criterion (AIC; \citealt{Aka73}), we derive an AIC-type criterion for propensity score analysis. Here, we define a risk function based on the Kullback-Leibler divergence as the cornerstone of the information criterion and treat a general causal inference model that is not necessarily a linear one. The causal effects to be estimated are those in the general population, such as the average treatment effect on the treated or the average treatment effect on the untreated. In light of the fact that this field attaches importance to doubly robust estimation, which allows either the model of the assignment variable or the model of the outcome variable to be wrong, we make the information criterion itself doubly robust so that either one can be wrong and it will still be an asymptotically unbiased estimator of the risk function. In simulation studies, we compare the derived criterion with an existing criterion obtained from a formal argument and confirm that the former outperforms the latter. Specifically, we check that the divergence between the estimated structure from the derived criterion and the true structure is clearly small in all simulation settings and that the probability of selecting the true or nearly true model is clearly higher. Real data analyses confirm that the results of variable selection using the two criteria differ significantly.