Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
部分识别因果效应的模型无关协变量辅助推断AI 解读 ↓
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部分识别因果效应的模型无关协变量辅助推断
- 研究问题
- 当因果目标依赖不可观测的潜在结果联合分布而只能部分识别时，如何利用协变量收紧界限，同时避免辅助分布估计错误破坏推断？
- 方法与思路
- 作者利用最优运输对偶构造协变量辅助推断，兼容任意条件分布估计，并通过乘子自助法进行协变量与模型选择。
- 材料陈述的贡献
- 摘要称随机实验下可容忍任意不准确的初始估计，观察研究扩展具双重稳健性；在相应速率条件下可渐近无偏估计锐界。
- 阅读关注点
- 应核对适用目标量与对偶可行性、统一有效性的具体含义，以及辅助速率、锐界逼近和模型选择后有效性的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；模型无关不等于无需设计假设，摘要未列目标量范围或实际区间缩窄幅度。
原文摘要
Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper bounds; however, unless the covariates are discrete with relatively small support, this approach typically requires binning covariates or estimating the conditional distributions of potential outcomes given the covariates. Binning can result in substantial efficiency loss and become challenging to implement, even with a moderate number of covariates. Estimating conditional distributions, on the other hand, may yield invalid inference if the distributions are inaccurately estimated, such as when a misspecified model is used or when the covariates are high-dimensional. In this paper, we propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands. Our method, based on duality theory for optimal transport problems, has four key properties. First, in randomized experiments, our approach can wrap around any estimates of the conditional distributions and provide uniformly valid inference, even if the initial estimates are arbitrarily inaccurate. A simple extension of our method to observational studies is doubly robust in the usual sense. Second, if nuisance parameters are estimated at semiparametric rates, our estimator is asymptotically unbiased for the sharp bound. Third, we can apply the multiplier bootstrap to select covariates and models without sacrificing validity even if the true model is not selected. Finally, it is computationally efficient. In particular, our method and theory work for discrete and continuous potential outcomes. Overall, in three empirical applications, our method consistently reduces the width of estimated identified sets and confidence intervals without making additional structural assumptions.