14 papers
The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Marie Neubrander, Graham Tierney, Alexander Volfovsky
Estimating causal effects of linguistic properties from observational text is difficult because the same document can contain both the treatment of interest and the non-treatment t…
Towards Optimal Estimators for Randomized Control Trials
Harsh Parikh, Gabriel Levin-Konigsberg, Nilesh Tripuraneni +5
Randomized controlled trials (RCTs) are fundamental tools for causal inference across technology companies, pharmaceutical research, and federal agencies. While the standard differ…
Mind the Sim-to-Real Gap & Think Like a Scientist
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1
Suppose a planner has a pre-trained simulator of a sequential decision problem and the option to run real experiments in the field. The simulator is cheap to query but inherits con…
TEA-Time: Transporting Effects Across Time
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1
Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the trial was conducted. The literature on g…
Adaptive Policy Learning Under Unknown Network Interference
Aidan Gleich, Eric Laber, Alexander Volfovsky
Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interference among units and (ii) using t…
DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
Kateryna Husar, Alexander Volfovsky
Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, fo…