De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
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
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…