Counterfactual Density Estimation using Kernel Stein Discrepancies

ICLR 2025 Conference Submission108 Authors

Keywords: counterfactual density estimation, kernel Stein discrepancy, causal inference, kernel methods

Abstract: Causal effects are usually studied in terms of the means of counterfactual distributions, which may be insufficient in many scenarios. Given a class of densities known up to normalizing constants, we propose to model counterfactual distributions by minimizing kernel Stein discrepancies in a doubly robust manner. This enables the estimation of counterfactuals over large classes of distributions while exploiting the desired double robustness. We present a theoretical analysis of the proposed estimator, providing sufficient conditions for consistency and asymptotic normality, as well as an examination of its empirical performance.

Submission Number: 108