论文标题
使用深层生成模型的有条件仪器的因果推断
Causal Inference with Conditional Instruments using Deep Generative Models
论文作者
论文摘要
仪器变量（IV）方法是一种广泛使用的方法，用于估计治疗对与潜在混杂因素的观察数据的兴趣结果的因果关系。预期标准IV与治疗变量有关，并且独立于系统中的所有其他变量。但是，由于严格的条件，直接从数据中搜索标准IV是一项挑战。已经提出了条件IV（CIV）方法，以使变量成为一组变量的仪器条件，从而可以更广泛地选择可能的IV，并启用IV方法的更广泛的实际应用。然而，没有数据驱动的方法可以直接从数据中发现CIV及其条件集。为了填补这一空白，在本文中，我们建议从与潜在混杂因素的数据中了解CIV信息及其条件集的表示形式，以进行平均因果效应估计。通过利用深刻的生成模型，我们开发了一种新型的数据驱动方法，以同时从测量变量中学习CIV的表示，并在给定的变量给定的条件集的表示形式。关于合成和现实世界数据集的广泛实验表明，我们的方法的表现优于现有的IV方法。
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.
