Puli Aahlad, Ranganath Rajesh
Computer Science, New York University.
Computer Science, Center for Data Science, New York University.
Adv Neural Inf Process Syst. 2020 Dec;33:8440-8451.
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed from treatment and IV that help estimate effects, called control functions. We characterize general control functions for effect estimation in a meta-identification result. Then, we show that structural assumptions on the treatment process allow the construction of general control functions, thereby guaranteeing identification. To construct general control functions and estimate effects, we develop the general control function method (GCFN). GCFN's first stage called variational decoupling (VDE) constructs general control functions by recovering the residual variation in the treatment given the IV. Using VDE's control function, GCFN's second stage estimates effects via regression. Further, we develop semi-supervised GCFN to construct general control functions using subsets of data that have both IV and confounders observed as supervision; this needs no structural treatment process assumptions. We evaluate GCFN on low and high dimensional simulated data and on recovering the causal effect of slave export on modern community trust [30].
因果效应估计依赖于将结果的变化分为由处理因素和混杂因素导致的部分。为了实现这种分离，从业者通常会使用仅影响处理因素的外部随机性来源，即所谓的工具变量（IV）。我们研究由处理因素和工具变量构建的有助于估计效应的变量，即控制函数。我们在一个元识别结果中刻画了用于效应估计的一般控制函数。然后，我们表明对处理过程的结构假设允许构建一般控制函数，从而保证识别。为了构建一般控制函数并估计效应，我们开发了一般控制函数方法（GCFN）。GCFN的第一阶段称为变分解耦（VDE），通过恢复给定工具变量时处理因素中的残余变化来构建一般控制函数。利用VDE的控制函数，GCFN的第二阶段通过回归估计效应。此外，我们开发了半监督GCFN，使用同时观察到工具变量和混杂因素的数据子集作为监督来构建一般控制函数；这不需要处理过程的结构假设。我们在低维和高维模拟数据上以及在恢复奴隶出口对现代社区信任的因果效应方面对GCFN进行了评估[30]。
