Counterfactual Density Estimation using Kernel Stein Discrepancies
Methodology
2024-02-20 v2
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.
Cite
@article{arxiv.2309.16129,
title = {Counterfactual Density Estimation using Kernel Stein Discrepancies},
author = {Diego Martinez-Taboada and Edward H. Kennedy},
journal= {arXiv preprint arXiv:2309.16129},
year = {2024}
}