Weight Uncertainty in Individual Treatment Effect
Keywords: Individual Treatment Effect, Causal inference, Bayesian inference
Abstract: The estimation of individual treatment effects (ITE) has recently gained significant attention from both the research and industrial communities due to its potential applications in various fields such as healthcare, economics, and education. However, the sparsity of observational data often leads to a lack of robustness and over-fitting in most existing methods. To address this issue, this paper investigates the benefits of incorporating uncertainty modeling in the process of optimizing parameters for robust ITE estimation. Specifically, we derive an informative generalization bound that connects to Bayesian inference and propose a variational bound in closed form to learn a probability distribution on the weights of a hypothesis and representation function. Through experiments on one synthetic dataset and two benchmark datasets, we demonstrate the effectiveness of our proposed model in comparison to state-of-the-art methods. Moreover, we conduct experiments on a real-world dataset in recommender scenarios to verify the benefits of uncertainty in causal inference. The results of our experiments provide evidence of the practicality of our model, which aligns with our initial expectations.
Primary Area: causal reasoning
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Submission Number: 8800
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