Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it's an art rather than science to find valid IVs in many real-world scenes. Moreover, the predefined hand-made IVs could be weak or erroneous by violating the conditions of valid IVs. These thorny facts hinder the application of the IV-based counterfactual prediction methods. In this paper, we propose a novel Automatic Instrumental Variable decomposition (AutoIV) algorithm to automatically generate representations serving the role of IVs from observed variables (IV candidates). Specifically, we let the learned IV representations satisfy the relevance condition with the treatment and exclusion condition with the outcome via mutual information maximization and minimization constraints, respectively. We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome. The IV and confounder representations compete for the information with their constraints in an adversarial game, which allows us to get valid IV representations for IV-based counterfactual prediction. Extensive experiments demonstrate that our method generates valid IV representations for accurate IV-based counterfactual prediction.
翻译:工具变量(IVs)是有条件地与结果无关的治疗随机性来源,在与未观察到的困惑者进行因果推断方面起着重要作用。然而,现有的基于IV的反事实预测方法需要精确定义的四分法,而在许多现实世界的场景中,它是一种艺术而不是科学,可以找到有效的四分法。此外,预先定义的手工制作的四分法可能因违反有效的四分法的条件而软弱或错误。这些棘手的事实妨碍了采用基于IV的反事实预测方法。在本文件中,我们提议采用新的自动仪器变量变异(AutIV)算法,以自动生成代表,服务于观察到的变量(IV候选者)的作用。具体地说,我们让所学的四分解法通过相互信息最大化和尽量减少限制,分别满足了治疗和排斥条件的相关性条件。我们还通过鼓励它们与治疗和结果相关联,来了解更深层的表达方式。四分解法在对抗性游戏中为信息与限制进行竞争,这使我们能够获得有效的四分辨的反向预测。