
Fairness, Welfare, and Equity in Personalized Pricing
We study the interplay of fairness, welfare, and equity considerations i...
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The Variational Method of Moments
The conditional moment problem is a powerful formulation for describing ...
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Rejoinder: New Objectives for Policy Learning
I provide a rejoinder for discussion of "More Efficient Policy Learning ...
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Fast Rates for Contextual Linear Optimization
Incorporating side observations of predictive features can help reduce u...
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Optimal OffPolicy Evaluation from Multiple Logging Policies
We study offpolicy evaluation (OPE) from multiple logging policies, eac...
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Stochastic Optimization Forests
We study conditional stochastic optimization problems, where we leverage...
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Offpolicy Evaluation in InfiniteHorizon Reinforcement Learning with Latent Confounders
Offpolicy evaluation (OPE) in reinforcement learning is an important pr...
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Doubly Robust OffPolicy Value and Gradient Estimation for Deterministic Policies
Offline reinforcement learning, wherein one uses offpolicy data logged ...
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Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning
We study the efficient offpolicy evaluation of natural stochastic polic...
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On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and DesignBased Inference
I study the minimaxoptimal design for a twoarm controlled experiment w...
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DTR Bandit: Learning to Make ResponseAdaptive Decisions With Low Regret
Dynamic treatment regimes (DTRs) for are personalized, sequential treatm...
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Comment: Entropy Learning for Dynamic Treatment Regimes
I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Z...
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On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
We study the problem of estimating treatment effects when the outcome of...
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Efficient Policy Learning from SurrogateLoss Classification Reductions
Recent work on policy learning from observational data has highlighted t...
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ConfoundingRobust Policy Evaluation in InfiniteHorizon Reinforcement Learning
Offpolicy evaluation of sequential decision policies from observational...
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Statistically Efficient OffPolicy Policy Gradients
Policy gradient methods in reinforcement learning update policy paramete...
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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Practitioners in diverse fields such as healthcare, economics and educat...
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Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond
We consider the efficient estimation of a lowdimensional parameter in t...
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Balanced Policy Evaluation and Learning for Right Censored Data
Individualized treatment rules can lead to better health outcomes when p...
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Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
Many scientific questions require estimating the effects of continuous t...
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Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in InfiniteHorizon Processes
Offpolicy evaluation (OPE) in reinforcement learning is notoriously dif...
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Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes
We study a nonparametric contextual bandit problem where the expected re...
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Double Reinforcement Learning for Efficient OffPolicy Evaluation in Markov Decision Processes
Offpolicy evaluation (OPE) in reinforcement learning allows one to eval...
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Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching
In causal inference, a variety of causal effect estimands have been stud...
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Policy Evaluation with Latent Confounders via Optimal Balance
Evaluating novel contextual bandit policies using logged data is crucial...
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More Efficient Policy Learning via Optimal Retargeting
Policy learning can be used to extract individualized treatment regimes ...
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Intrinsically Efficient, Stable, and Bounded OffPolicy Evaluation for Reinforcement Learning
Offpolicy evaluation (OPE) in both contextual bandits and reinforcement...
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Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Personalized interventions in social services, education, and healthcare...
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Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
The increasing impact of algorithmic decisions on people's lives compels...
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DataPooling in Stochastic Optimization
Managing largescale systems often involves simultaneously solving thous...
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Deep Generalized Method of Moments for Instrumental Variable Analysis
Instrumental variable analysis is a powerful tool for estimating causal ...
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The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Where machinelearned predictive risk scores inform highstakes decision...
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Classifying Treatment Responders Under Causal Effect Monotonicity
In the context of individuallevel causal inference, we study the proble...
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Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Assessing the fairness of a decision making system with respect to a pro...
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More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions
Inverse probability of treatment weighting (IPTW), which has been used t...
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Removing Hidden Confounding by Experimental Grounding
Observational data is increasingly used as a means for making individual...
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Interval Estimation of IndividualLevel Causal Effects Under Unobserved Confounding
We study the problem of learning conditional average treatment effects (...
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Residual Unfairness in Fair Machine Learning from Prejudiced Data
Recent work in fairness in machine learning has proposed adjusting for f...
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Optimal Balancing of TimeDependent Confounders for Marginal Structural Models
Marginal structural models (MSMs) estimate the causal effect of a timev...
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Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Valid causal inference in observational studies often requires controlli...
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ConfoundingRobust Policy Improvement
We study the problem of learning personalized decision policies from obs...
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Learning Weighted Representations for Generalization Across Designs
Predictive models that generalize well under distributional shift are of...
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Policy Evaluation and Optimization with Continuous Treatments
We study the problem of policy evaluation and learning from batched cont...
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DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
We study optimal covariate balance for causal inferences from observatio...
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Balanced Policy Evaluation and Learning
We present a new approach to the problems of evaluating and learning per...
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InstrumentArmed Bandits
We extend the classic multiarmed bandit (MAB) model to the setting of n...
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Generalized Optimal Matching Methods for Causal Inference
We develop an encompassing framework for matching, covariate balancing, ...
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Dynamic Assortment Personalization in High Dimensions
We study the problem of dynamic assortment personalization with large, h...
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Recursive Partitioning for Personalization using Observational Data
We study the problem of learning to choose from m discrete treatment opt...
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Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices
We consider the problem of learning the preferences of a heterogeneous p...
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Nathan Kallus
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