7 citations · 7 across the 1 of their papers we have counts for
8 papers
Learning from a Biased Sample
Roshni Sahoo, Lihua Lei, Stefan Wager
The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decisi…
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…
Experimenting under Stochastic Congestion
Shuangning Li, Ramesh Johari, Xu Kuang +1
We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply or excess demand. Such congestion gives rise to cross-unit…
Switchback Experiments under Geometric Mixing
Yuchen Hu, Stefan Wager
The switchback is an experimental design that measures treatment effects by repeatedly turning an intervention on and off for a whole system. Switchback experiments are a robust wa…
Noise-Induced Randomization in Regression Discontinuity Designs
Dean Eckles, Nikolaos Ignatiadis, Stefan Wager +1
Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. Here we…
Policy Learning with Competing Agents
Roshni Sahoo, Stefan Wager
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to s…