Counterfactual Learning under Rank Preservation

Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo

Keywords: Counterfactual Inference, Causal Inference, Identifiability

TL;DR: We introduce the rank preservation assumption to identify counterfactual outcomes, proposing a novel ideal loss for theoretically unbiased learning and a kernel-based estimator for empirical estimation.

Abstract: Counterfactual inference aims to estimate the counterfactual outcome given knowledge of an observed treatment and the factual outcome, with broad applications in fields such as epidemiology, econometrics, and management science. In this paper, we propose a principled approach for identifying and estimating the counterfactual outcome. Specifically, we introduce a simple and intuitive rank preservation assumption to identify the counterfactual outcome without relying on a known structural causal model. Building on this, we propose a novel ideal loss for theoretically unbiased learning of the counterfactual outcome and further develop a kernel-based estimator for its empirical estimation. Our theoretical analysis shows that the proposed ideal loss is convex, and the proposed estimator is unbiased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed method.

Primary Area: causal reasoning

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Submission Number: 8331