Causal Inference with High-Dimensional Treatments
高维处理下的因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论高维处理的潜在结局、双重稳健估计及因果效应估计限制，直接属于因果推断。
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高维处理下的因果推断
- 研究问题
- 当多值处理的类别数或处理向量维度接近甚至超过样本量时，如何在重叠不足和有效样本较小的条件下估计潜在结局？
- 方法与思路
- 作者先讨论一致估计的基本限制，再在稀疏假设下构造双重稳健估计量，并推广为带约束回归的稀疏伪结局估计框架。
- 材料陈述的贡献
- 摘要称，给出有限样本风险界及稀疏模型中的极小极大下界，并将方法推广到不同高维处理和一般泛函的估计。
- 阅读关注点
- 应核对稀疏性究竟约束哪些对象、重叠违背如何被容纳，以及双重稳健性质、下界模型与向量处理推广之间的关系。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实。摘要未说明完整识别条件、稀疏程度及经验应用，最优性限于文中设定的模型。
原文摘要
In this work, we consider causal inference in various high-dimensional treatment settings, including for single multi-valued treatments and vector treatments with binary or continuous components, when the number of treatments can be comparable to or even larger than the number of observations. These settings bring unique challenges: first, the treatment effects of interest are represented by a high-dimensional vector rather than a scalar; second, positivity violations are often unavoidable; and third, estimation can be based on a smaller effective sample size. We first discuss fundamental limits of estimating effects here, showing that consistent estimation is impossible without further assumptions. We go on to propose novel doubly robust estimators for mean potential outcomes of a high-dimensional single multi-valued treatment. We analyze the proposed estimators under sparsity assumptions, giving finite-sample risk bounds and showing that consistent estimation is possible under these conditions. Moreover, we derive minimax lower bounds in a sparse and structure-agnostic model to characterize optimal rates of convergence and show our risk bounds are unimprovable. We then generalize our proposed estimators as a sparse pseudo-outcome regression framework with constrained regression estimators and error guarantees under sparsity, allowing estimation of generic functionals and different types of high-dimensional treatments. We apply the framework to derive estimators of the mean potential outcomes for high-dimensional vector treatments. Finally, we illustrate the proposed methods through a simulation and an empirical application.