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Counterfactual Density Estimation using Kernel Stein Discrepancies

Diego Martinez-Taboada · Edward Kennedy

Halle B #193
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Thu 9 May 1:45 a.m. PDT — 3:45 a.m. PDT


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.

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