[论文解读] Learning Individual Treatment Effects from Networked Observational Data.

Ruocheng Guo, Jundong Li+1 more|arXiv (Cornell University)|Jun 8, 2019

Advanced Causal Inference Techniques被引用 5

一句话总结

本文提出了网络去混杂（network deconfounder）方法，这是一种因果推断框架，利用网络结构识别并去除观测数据中的隐性混杂因素。通过从网络信息中学习表征，该方法可在不依赖不可检验的无混杂性假设的前提下，实现更精确的个体处理效应估计。

ABSTRACT

Convenient access to observational data enables us to learn causal effects without randomized experiments. This research direction draws increasing attention in research areas such as economics, healthcare, and education. For example, we can study how a medicine (the treatment) causally affects the health condition (the outcome) of a patient using existing electronic health records. To validate causal effects learned from observational data, we have to control confounding bias -- the influence of variables which causally influence both the treatment and the outcome. Existing work along this line overwhelmingly relies on the unconfoundedness assumption that there do not exist unobserved confounders. However, this assumption is untestable and can even be untenable. In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders. For example, in an observational study of the individual-level treatment effect of a medicine, instead of randomized experiments, the medicine is often assigned to each individual based on a series of factors. Some of the factors (e.g., socioeconomic status) can be challenging to measure and therefore become hidden confounders. Fortunately, the socioeconomic status of an individual can be reflected by whom she is connected in social networks. With this fact in mind, we aim to exploit the network information to recognize patterns of hidden confounders which would further allow us to learn valid individual causal effects from observational data. In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations to unravel patterns of hidden confounders from the network information. Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.

研究动机与目标

• 为解决因果推断中观测数据的无混杂性假设局限性，该假设不可检验且在实践中往往不成立。
• 利用网络信息（如社交关系）推断影响处理分配和结果的未观测混杂因素模式。
• 开发一种框架，即使存在隐性混杂因素，也能实现对个体水平处理效应的有效估计。
• 在具有网络结构的多样化观测数据集中，实证验证所提出方法的有效性。

提出的方法

• 网络去混杂方法从网络结构中学习潜在表征，以建模不可直接观测的隐性混杂因素模式。
• 通过假设具有相似网络位置的个体可能受相似未测量因素的影响，利用网络拓扑推断未观测混杂因素。
• 在网络上应用表示学习技术，生成捕捉相连个体间共享混杂影响的嵌入表征。
• 将这些学习到的表征整合进因果推断模型，以调整估计个体处理效应时的隐性混杂影响。
• 该方法设计为可扩展，适用于现实世界中的观测数据，其中处理分配受未测量因素（如社会经济地位）影响。
• 无需显式测量混杂因素，而是依赖关系结构推断其存在及其影响。

实验结果

研究问题

• RQ1 是否可以利用网络信息推断影响观测研究中处理分配和结果的隐性混杂因素？
• RQ2 从网络结构中学习到的表征在多大程度上能提升个体处理效应估计的准确性？
• RQ3 当存在隐性混杂因素时，网络去混杂方法与标准因果推断方法在无混杂性假设下的表现如何比较？
• RQ4 在哪些类型的现实世界数据集中，网络去混杂方法表现出稳健性能？

主要发现

• 网络去混杂方法通过利用网络结构识别并调整隐性混杂因素，成功降低了个体处理效应估计的偏差。
• 在多个数据集上的实证评估表明，当存在隐性混杂因素时，该方法优于假设无混杂性的标准方法。
• 即使混杂因素（如社会经济地位）未被测量，但其影响反映在社交网络连接中，该框架仍能提升估计准确性。
• 学习到的网络表征有效捕捉了潜在混杂模式，从而在观测情境下实现更可靠的因果推断。