148 citations · 972 across the 92 of their papers we have counts for
8 papers · 1 filter
An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls
Angela Zhou, Andrew Koo, Nathan Kallus +4
We conduct an empirical evaluation of the impact of New York's bail reform on crime. New York State's Bail Elimination Act went into effect on January 1, 2020, eliminating money ba…
Stateful Offline Contextual Policy Evaluation and Learning
Nathan Kallus, Angela Zhou
We study off-policy evaluation and learning from sequential data in a structured class of Markov decision processes that arise from repeated interactions with an exogenous sequence…
Residual Overfit Method of Exploration
James McInerney, Nathan Kallus
Exploration is a crucial aspect of bandit and reinforcement learning algorithms. The uncertainty quantification necessary for exploration often comes from either closed-form expres…
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
Andrew Bennett, Nathan Kallus
In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobser…
Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models
Guido Imbens, Nathan Kallus, Xiaojie Mao
We develop a new approach for identifying and estimating average causal effects in panel data under a linear factor model with unmeasured confounders. Compared to other methods tac…
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
We study the estimation of causal parameters when not all confounders are observed and instead negative controls are available. Recent work has shown how these can enable identific…