Semiparametric Estimation on Multi-treatment Causal Effects via Cross-Fitting
通过交叉拟合进行多处理因果效应的半参数估计AI 解读 ↓
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通过交叉拟合进行多处理因果效应的半参数估计
- 研究问题
- 在观察数据包含多个处理选项时，如何将二元处理的双重稳健交叉拟合推广到多处理比较，并提供同时统计推断？
- 方法与思路
- 作者在潜在结果框架下提出广义交叉拟合估计量 GCF，允许机器学习估计辅助部分，并研究渐近性质和同时置信区间。
- 材料陈述的贡献
- 摘要称，该方法将二元处理交叉拟合推广到多处理比较，给出理论证明，建立效率相关结论，并以模拟评价估计表现。
- 阅读关注点
- 应核对多处理效应的比较目标、处理分配与辅助估计条件，以及半参数效率结论和同时置信区间覆盖范围的准确表述。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出所谓较弱假设、具体效率条件或模拟数值，不能判断实际优势。
原文摘要
Causal inference is a critical research area with multi-disciplinary origins and applications, ranging from statistics, computer science, economics, psychology to public health. In many scientific research, randomized experiments provide a golden standard for estimation of causal effects for decades. However, in many situations, randomized experiments are not feasible in practice so that practitioners need to rely on empirical investigation for causal reasoning. Causal inference via observational data is a challenging task since the knowledge of the treatment assignment mechanism is missing, which typically requires non-testable assumptions to make the inference possible. For several years, great effort has been devoted to the research of causal inference for binary treatments. In practice, it is also common to use observational data on multiple treatment comparisons. Within the potential outcomes framework, we propose a generalized cross-fitting estimator (GCF), which generalizes the doubly robust estimator with cross-fitting for binary treatment to multiple treatment comparisons and provides rigorous proofs on its statistical properties. This estimator permits the use of more flexible machine learning methods to model the nuisance parts, and based on relatively weak assumptions, while there is still a theoretical guarantee for valid statistical inference. We show the asymptotic properties of the GCF estimators, and provide the asymptotic simultaneous confidence intervals that achieve the semiparametric efficiency bound for average treatment effect. The performance of the estimator is accessed through simulation study based on the common evaluation metrics generally considered in the causal inference literature.