Robust Causal Inference for Recommender System to Overcome Noisy Confounders

Abstract

Recently, there has been growing interest in integrating causal inference into recommender systems to answer the hypothetical question: "what would be the potential feedback when a user is recommended a product?" Various unbiased estimators, including Inverse Propensity Score (IPS) and Doubly Robust (DR), have been proposed to address this question. However, these estimators often assume that confounders are precisely observable, which is not always the case in real-world scenarios. To address this challenge, we propose a novel method called Adversarial Training-based IPS (AT-IPS), which uses adversarial training to handle noisy confounders. The proposed method defines a feasible region for the confounders, obtains the worst-case noise (adversarial noise) within the region, and jointly trains the propensity model and the prediction model against such noise to improve their robustness. We provide a theoretical analysis of the accuracy-robustness tradeoff of AT-IPS and demonstrate its superior performance compared to other popular estimators on both real-world and semi-synthetic datasets.