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.

翻译：独立成分分析（ICA）利用非高斯性度量从数据中识别潜在源并估计其混合系数（Shimizu等人，2006）。与此同时，高阶正交机器学习（OML）利用非高斯处理噪声，在存在混杂干扰效应的情况下提供更准确的处理效应估计（Mackey等人，2018）。值得注意的是，我们发现这两种方法依赖于相同的矩条件来实现一致估计。我们继而利用这一关联，展示了ICA如何能有效用于处理效应估计。具体而言，我们证明了线性ICA能够一致地估计多重处理效应，即使存在高斯混杂因子，并识别出在哪些情况下ICA在处理效应估计上可证明比OML具有更高的样本效率。我们的合成需求估计实验验证了该理论，并证明线性ICA即使在存在非线性干扰的情况下也能准确估计处理效应。