论文标题
通过相互信息最小化学习反事实回归的删除表示形式
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization
论文作者
论文摘要
学习个人水平的治疗效果是因果推断的一个基本问题，并且在许多领域，尤其是在许多涉及许多互联网公司的用户增长领域中受到了越来越多的关注。最近，将协变量分解为三个潜在因素，包括工具性，混杂和调整因素在内的三个潜在因素，在治疗效应估计中取得了巨大成功。但是，如何精确地学习潜在的分离因素仍然是一个开放的问题。具体而言，以前的方法无法获得独立的分解因素，这是识别治疗效果的必要条件。在本文中，我们提出了通过相互信息最小化（MIM-DRCFR）进行反事实回归的分离表示，该信息使用多任务学习框架在学习潜在因素时共享信息，并纳入MI最小化学习标准以确保这些因素的独立性。包括公共基准和现实世界用户增长数据集在内的广泛实验表明，我们的方法的性能要比最先进的方法要好得多。
Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding and adjustment factors, have witnessed great success in treatment effect estimation. However, it remains an open problem how to learn the underlying disentangled factors precisely. Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Extensive experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.
