用独立成分分析估计因果效应，突破高斯混杂限制。
Estimating Treatment Effects with Independent Component Analysis
- 基于非高斯性构造新方法，统一处理因果与信号分离
- 线性ICA可准确估计多重处理效应，样本效率优于OML
- 适合存在非线性干扰的因果推断场景
独立成分分析（ICA）利用非高斯性从数据中识别潜在源并估计其混合系数（Shimizu等，2006）。与此同时，高阶正交机器学习（OML）利用非高斯处理噪声，在存在混淆干扰时提供更精确的处理效应估计（Mackey等，2018）。我们发现，这两种方法依赖相同的矩条件实现一致估计。由此出发，我们证明线性ICA在存在高斯混杂的情况下仍可一致估计多个处理效应，并识别出ICA在处理效应估计中比OML更具样本效率的区间。合成需求估计实验验证了该理论，表明线性ICA即使在存在非线性干扰时也能准确估计处理效应。
原文摘要 · Abstract (English)
Independent Component Analysis (ICA) uses a measure of non-Gaussianity to identify latent sources from data and estimate their mixing coefficients (Shimizu et al., 2006). Meanwhile, higher-order Orthogonal Machine Learning (OML) exploits non-Gaussian treatment noise to provide more accurate estimates of treatment effects in the presence of confounding nuisance effects (Mackey et al., 2018). Remarkably, we find that the two approaches rely on the same moment conditions for consistent estimation. We then seize upon this connection to show how ICA can be effectively used for treatment effect estimation. Specifically, we prove that linear ICA can consistently estimate multiple treatment effects, even in the presence of Gaussian confounders, and identify regimes in which ICA is provably more sample-efficient than OML for treatment effect estimation. Our synthetic demand estimation experiments confirm this theory and demonstrate that linear ICA can accurately estimate treatment effects even in the presence of nonlinear nuisance.
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