De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
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通过生成对抗网络学习去混杂表征以推断连续处理的反事实
- 研究问题
- 在连续处理的反事实预测中，如何学习与处理变量解除线性及非线性依赖的协变量表征，并保留预测反事实结果所需信息？
- 方法与思路
- 作者提出非参数去混杂表征学习框架，通过对抗训练降低表征与处理的依赖，并嵌入反事实推断网络联合学习结果预测。
- 材料陈述的贡献
- 摘要称该框架在合成数据上优于所比较方法，并在 MIMIC 医疗数据中分析红细胞分布宽度与死亡率之间的关系。
- 阅读关注点
- 应核对表征去依赖与因果识别之间的论证、训练目标是否保留必要混杂信息，以及合成实验和医疗应用如何评估反事实有效性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明完整识别假设，去相关和预测表现本身不足以确认因果关系。
原文摘要
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are already some sample reweighting methods based on Marginal Structural Model for eliminating the confounding bias, they generally focus on removing the treatment's linear dependence on confounders and rely on the accuracy of the assumed parametric models, which are usually unverifiable. In this paper, we propose a de-confounding representation learning (DRL) framework for counterfactual outcome estimation of continuous treatment by generating the representations of covariates disentangled with the treatment variables. The DRL is a non-parametric model that eliminates both linear and nonlinear dependence between treatment and covariates. Specifically, we train the correlations between the de-confounded representations and the treatment variables against the correlations between the covariate representations and the treatment variables to eliminate confounding bias. Further, a counterfactual inference network is embedded into the framework to make the learned representations serve both de-confounding and trusted inference. Extensive experiments on synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables. In addition, we apply the DRL model to a real-world medical dataset MIMIC and demonstrate a detailed causal relationship between red cell width distribution and mortality.