11 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
Rejoinder: New Objectives for Policy Learning
Nathan Kallus
I provide a rejoinder for discussion of "More Efficient Policy Learning via Optimal Retargeting" to appear in the Journal of the American Statistical Association with discussion by…
More Efficient Policy Learning via Optimal Retargeting
Nathan Kallus
Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is…
Classifying Treatment Responders Under Causal Effect Monotonicity
Nathan Kallus
In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples…
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Nathan Kallus
We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks.…