As an important problem of causal inference, we discuss the estimation of treatment effects under the existence of unobserved confounding. By representing the confounder as a latent variable, we propose Counterfactual VAE, a new variant of variational autoencoder, based on recent advances in identifiability of representation learning. Combining the identifiability and classical identification results of causal inference, under mild assumptions on the generative model and with small noise on the outcome, we theoretically show that the confounder is identifiable up to an affine transformation and then the treatment effects can be identified. Experiments on synthetic and semi-synthetic datasets demonstrate that our method matches the state-of-the-art, even under settings violating our formal assumptions.
翻译:作为因果推断的一个重要问题,我们讨论了在未观察到的混乱存在的情况下对治疗效果的估计问题。通过将困惑者作为潜在变量来代表,我们提议反事实VAE,这是基于最近代表性学习的可识别性进展的变异自动编码新变体。根据对遗传模型的轻度假设和结果的微小噪音,我们从理论上表明,混结者可以识别成一个折形变异,然后可以辨别处理效果。合成和半合成数据集实验表明,我们的方法符合最新技术,即使在违反我们正式假设的情况下也是如此。