27 citations · 110 across the 14 of their papers we have counts for
15 papers
A Human-Augmenting Agentic Workflow for Observational Causal Inference
Winston Chou, Adrien Alexandre, Lars Olds +2
Data analysis agents are becoming increasingly common tools for applied and scientific research. Yet, for highly specialized tasks such as Observational Causal Inference (OCI), hum…
Estimating Structural Disparities for Face Models
Shervin Ardeshir, Cristina Segalin, Nathan Kallus
In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoin…
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding
Jacob Dorn, Kevin Guo, Nathan Kallus
We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omit…
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…
On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference
Nathan Kallus
I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes may vary in a given set. When this set is permutation symmetric, the optimal…
Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
Nathan Kallus, Michele Santacatterina
Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most c…