activity
20122023
most citedDropout Training as Adaptive Regularization

260 citations · 828 across the 46 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

econ.EM2021★ 6 cited

Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations

Xinkun Nie, Guido Imbens, Stefan Wager

The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions. Such generaliza…

stat.ME2021

Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

Steve Yadlowsky, Scott Fleming, Nigam Shah +2

There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. W…

cs.LG2021★ 2 cited

Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorability

Yuchen Hu, Stefan Wager

We consider off-policy evaluation of dynamic treatment rules under sequential ignorability, given an assumption that the underlying system can be modeled as a partially observed Ma…

econ.EM2021★ 14 cited

Treatment Effects in Market Equilibrium

Evan Munro, Xu Kuang, Stefan Wager

Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to…

stat.ME2021

Average Direct and Indirect Causal Effects under Interference

Yuchen Hu, Shuangning Li, Stefan Wager

We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is…

stat.ME2021★ 7 cited

Treatment Allocation under Uncertain Costs

Georgy Kalashnov, Evan Munro, Hao Sun +2

We consider the problem of learning how to optimally allocate treatments whose cost is uncertain and can vary with pre-treatment covariates. This setting may arise in medicine if w…