## Publications

“Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions,” to appear in* Proceedings of the 37th Conference on Neural Information Processing Systems *(NeurIPS, 2023).

(with Abhineet Agarwal and Anish Agarwal)

We study how to learn the effects of combinations of treatments across heterogeneous units. We propose a new estimator that uses latent similarity between potential outcomes (i.e., approximate factor structure) as well as restrictions on how treatments interact: for example, two sets of treatments with a large overlap should have similar effects. We give conditions on the design that allow consistent estimation and show that they are satisfied for a particular class of randomized experimental designs. Both in theory and in simulations, this results in a substantial error reduction relative to existing methods.

“Can Calibration and Equal Error Rates be Reconciled?” *Proceedings of the 2nd Symposium on Foundations of Responsible Computing* (FORC 2021).

(with Claire Lazar-Reich)

We revisit a foundational result in algorithmic fairness on the incompatibility of equal error rates and group-wise calibration. Instead, we consider the information design problem of providing a calibrated risk score to a rational decision maker, so that resulting decisions have equal error rates. We exactly characterize when such scores can be constructed, and devise a simple post-processing algorithm (based upon convex programming) to maximize precision of the risk score under the necessary constraints. Finally, we successfully apply our post-processing to the COMPAS pre-trial risk assessment algorithm.

“Localization, Convexity and Star Aggregation,” *Proceedings of the 35th Conference on Neural Information Processing Systems* (NeurIPS, 2021).

We study the method of offset Rademacher complexities, a powerful tool for deriving statistical guarantees for high-dimensional and non-parametric estimators. By novel geometric arguments, we extend the technique to settings where the model is non-convex and where the loss is not strongly convex. As applications, we provide a sharp analysis of non-parametric logistic regression and regression with *p*-loss.