6 citations · 13 across the 6 of their papers we have counts for
7 papers · 1 filter
Doubly robust treatment effect estimation with missing attributes
Imke Mayer, Erik Sverdrup, Tobias Gauss +3
Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper, we consider various sets of assumptions under which causal infer…
Cross-Validation, Risk Estimation, and Model Selection
Stefan Wager
Cross-validation is a popular non-parametric method for evaluating the accuracy of a predictive rule. The usefulness of cross-validation depends on the task we want to employ it fo…
Covariate-Powered Empirical Bayes Estimation
Nikolaos Ignatiadis, Stefan Wager
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect…
Sparsity Double Robust Inference of Average Treatment Effects
Jelena Bradic, Stefan Wager, Yinchu Zhu
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to val…
Learning When-to-Treat Policies
Xinkun Nie, Emma Brunskill, Stefan Wager
Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medic…
Experimenting in Equilibrium
Stefan Wager, Kuang Xu
Classical approaches to experimental design assume that intervening on one unit does not affect other units. There are many important settings, however, where this non-interference…