Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
平均因果效应识别假设的比较：稳健性与半参数效率AI 解读 ↓
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平均因果效应识别假设的比较：稳健性与半参数效率
- 研究问题
- 当后门、前门和同时使用处理前协变量与中介的双门识别假设都可能合理时，选择不同模型会怎样影响平均因果效应估计的效率与稳健性？
- 方法与思路
- 作者推导三种识别假设及其组合下的有效影响函数和效率界，研究影响函数估计方程何时达到界，以及对干扰模型误设的稳健性。
- 材料陈述的贡献
- 摘要称，没有一种识别模型在所有情形下效率最高；作者给出部分效率界的比较条件，并展示识别假设选择涉及效率与误设稳健性的权衡。
- 阅读关注点
- 应核对三种识别假设的精确定义、组合模型的限制，以及效率界比较条件；还需检查各估计量允许哪些干扰模型误设和有限样本表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未给出具体假设、效率界公式或模拟数值，不能据此为实际应用指定普遍最优的识别方案。
原文摘要
Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying assumptions may be plausible; an analyst has to make a delicate choice between these models. In this paper, we study three identifying assumptions based on the potential outcome framework: the back-door assumption, which uses pre-treatment covariates, the front-door assumption, which uses mediators, and the two-door assumption using pre-treatment covariates and mediators simultaneously. We provide the efficient influence functions and the corresponding semiparametric efficiency bounds that hold under these assumptions, and their combinations. We demonstrate that neither of the identification models provides uniformly the most efficient estimation and give conditions under which some bounds are lower than others. We show when semiparametric estimating equation estimators based on influence functions attain the bounds, and study the robustness of the estimators to misspecification of the nuisance models. The theory is complemented with simulation experiments on the finite sample behavior of the estimators. The results obtained are relevant for an analyst facing a choice between several plausible identifying assumptions and corresponding estimators. Our results show that this choice implies a trade-off between efficiency and robustness to misspecification of the nuisance models.