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stat.ME2026
Gaussian-efficient testing by betting on the mean of bounded data
Diego Martinez-Taboada, Aaditya Ramdas
Given -valued random variables such that for all , we propose a new nonasymptotic confidence interval for tha…
stat.ME2023
Counterfactual Density Estimation using Kernel Stein Discrepancies
Diego Martinez-Taboada, Edward H. Kennedy
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 normal…
stat.ME2023
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Diego Martinez-Taboada, Aaditya Ramdas, Edward H. Kennedy
The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments.…