论文标题
反事实$χ$ - gan
The Counterfactual $χ$-GAN
论文作者
论文摘要
因果推论通常依赖于反事实框架，这要求治疗分配独立于结果，称为强烈的无知性。在观察数据的因果分析中强大的无知性的方法包括加权和匹配方法。效应估计值，例如平均治疗效果（ATE），然后被估计为在重新加权或匹配的分布中的期望，p。 P的选择很重要，可以影响效应估计值和效应估计方差的解释。在这项工作中，我们没有指定P，而是学会了一个分布，该分布同时使覆盖范围最大化并最大程度地减少了ATE估计的差异。为了学习此分布，这项研究提出了一个称为反事实$χ$ -GAN（CGAN）的基于生成的对抗网络（GAN）的模型，该模型还学会了功能平衡的权重，并支持在没有偶然混淆的情况下支持无偏的因果估计。我们的模型将Pearson $χ^2 $ Divergence最小化，我们同时显示的是覆盖范围并最大程度地减少了重要性采样估计的差异。据我们所知，这是Pearson $χ^2 $ Divergence的第一次应用。我们证明了CGAN在模拟和现实医学数据中相对于已建立的加权方法实现特征平衡的有效性。
Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the reweighted or matched distribution, P . The choice of P is important and can impact the interpretation of the effect estimate and the variance of effect estimates. In this work, instead of specifying P, we learn a distribution that simultaneously maximizes coverage and minimizes variance of ATE estimates. In order to learn this distribution, this research proposes a generative adversarial network (GAN)-based model called the Counterfactual $χ$-GAN (cGAN), which also learns feature-balancing weights and supports unbiased causal estimation in the absence of unobserved confounding. Our model minimizes the Pearson $χ^2$ divergence, which we show simultaneously maximizes coverage and minimizes the variance of importance sampling estimates. To our knowledge, this is the first such application of the Pearson $χ^2$ divergence. We demonstrate the effectiveness of cGAN in achieving feature balance relative to established weighting methods in simulation and with real-world medical data.
