b'Nathan Kallus'

Fairness, Welfare, and Equity in Personalized Pricing

We study the interplay of fairness, welfare, and equity considerations i...
12/21/2020 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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The Variational Method of Moments

The conditional moment problem is a powerful formulation for describing ...
12/17/2020 ∙ by Andrew Bennett, et al. ∙ 7 ∙ share

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Rejoinder: New Objectives for Policy Learning

I provide a rejoinder for discussion of "More Efficient Policy Learning ...
12/05/2020 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Fast Rates for Contextual Linear Optimization

Incorporating side observations of predictive features can help reduce u...
11/05/2020 ∙ by Yichun Hu, et al. ∙ 1 ∙ share

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Optimal Off-Policy Evaluation from Multiple Logging Policies

We study off-policy evaluation (OPE) from multiple logging policies, eac...
10/21/2020 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Stochastic Optimization Forests

We study conditional stochastic optimization problems, where we leverage...
08/17/2020 ∙ by Nathan Kallus, et al. ∙ 21 ∙ share

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Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

Off-policy evaluation (OPE) in reinforcement learning is an important pr...
07/27/2020 ∙ by Andrew Bennett, et al. ∙ 5 ∙ share

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Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies

Offline reinforcement learning, wherein one uses off-policy data logged ...
06/06/2020 ∙ by Nathan Kallus, et al. ∙ 15 ∙ share

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Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning

We study the efficient off-policy evaluation of natural stochastic polic...
06/06/2020 ∙ by Nathan Kallus, et al. ∙ 4 ∙ share

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On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference

I study the minimax-optimal design for a two-arm controlled experiment w...
05/06/2020 ∙ by Nathan Kallus, et al. ∙ 4 ∙ share

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DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret

Dynamic treatment regimes (DTRs) for are personalized, sequential treatm...
05/06/2020 ∙ by Yichun Hu, et al. ∙ 5 ∙ share

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Comment: Entropy Learning for Dynamic Treatment Regimes

I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Z...
04/06/2020 ∙ by Nathan Kallus, et al. ∙ 3 ∙ share

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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...
03/27/2020 ∙ by Nathan Kallus, et al. ∙ 2 ∙ share

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Efficient Policy Learning from Surrogate-Loss Classification Reductions

Recent work on policy learning from observational data has highlighted t...
02/12/2020 ∙ by Andrew Bennett, et al. ∙ 11 ∙ share

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Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning

Off-policy evaluation of sequential decision policies from observational...
02/11/2020 ∙ by Nathan Kallus, et al. ∙ 8 ∙ share

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Statistically Efficient Off-Policy Policy Gradients

Policy gradient methods in reinforcement learning update policy paramete...
02/10/2020 ∙ by Nathan Kallus, et al. ∙ 26 ∙ share

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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...
01/21/2020 ∙ by Fredrik D. Johansson, et al. ∙ 89 ∙ share

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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 low-dimensional parameter in t...
12/30/2019 ∙ by Nathan Kallus, et al. ∙ 13 ∙ share

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Balanced Policy Evaluation and Learning for Right Censored Data

Individualized treatment rules can lead to better health outcomes when p...
11/13/2019 ∙ by Owen E. Leete, et al. ∙ 0 ∙ share

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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...
10/26/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes

Off-policy evaluation (OPE) in reinforcement learning is notoriously dif...
09/12/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes

We study a nonparametric contextual bandit problem where the expected re...
09/05/2019 ∙ by Yichun Hu, et al. ∙ 0 ∙ share

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Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes

Off-policy evaluation (OPE) in reinforcement learning allows one to eval...
08/22/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
08/13/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Policy Evaluation with Latent Confounders via Optimal Balance

Evaluating novel contextual bandit policies using logged data is crucial...
08/06/2019 ∙ by Andrew Bennett, et al. ∙ 0 ∙ share

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More Efficient Policy Learning via Optimal Retargeting

Policy learning can be used to extract individualized treatment regimes ...
06/20/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning

Off-policy evaluation (OPE) in both contextual bandits and reinforcement...
06/09/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds

Personalized interventions in social services, education, and healthcare...
06/04/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
06/01/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Data-Pooling in Stochastic Optimization

Managing large-scale systems often involves simultaneously solving thous...
06/01/2019 ∙ by Vishal Gupta, et al. ∙ 0 ∙ share

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Deep Generalized Method of Moments for Instrumental Variable Analysis

Instrumental variable analysis is a powerful tool for estimating causal ...
05/29/2019 ∙ by Andrew Bennett, et al. ∙ 0 ∙ share

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The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric

Where machine-learned predictive risk scores inform high-stakes decision...
02/15/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Classifying Treatment Responders Under Causal Effect Monotonicity

In the context of individual-level causal inference, we study the proble...
02/14/2019 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
11/27/2018 ∙ by Jiahao Chen, et al. ∙ 0 ∙ share

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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...
11/10/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Removing Hidden Confounding by Experimental Grounding

Observational data is increasingly used as a means for making individual...
10/27/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

We study the problem of learning conditional average treatment effects (...
10/05/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
06/07/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models

Marginal structural models (MSMs) estimate the causal effect of a time-v...
06/04/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Causal Inference with Noisy and Missing Covariates via Matrix Factorization

Valid causal inference in observational studies often requires controlli...
06/03/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Confounding-Robust Policy Improvement

We study the problem of learning personalized decision policies from obs...
05/22/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Learning Weighted Representations for Generalization Across Designs

Predictive models that generalize well under distributional shift are of...
02/23/2018 ∙ by Fredrik D. Johansson, et al. ∙ 0 ∙ share

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Policy Evaluation and Optimization with Continuous Treatments

We study the problem of policy evaluation and learning from batched cont...
02/16/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
02/15/2018 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Balanced Policy Evaluation and Learning

We present a new approach to the problems of evaluating and learning per...
05/21/2017 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Instrument-Armed Bandits

We extend the classic multi-armed bandit (MAB) model to the setting of n...
05/21/2017 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Generalized Optimal Matching Methods for Causal Inference

We develop an encompassing framework for matching, covariate balancing, ...
12/26/2016 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Dynamic Assortment Personalization in High Dimensions

We study the problem of dynamic assortment personalization with large, h...
10/18/2016 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Recursive Partitioning for Personalization using Observational Data

We study the problem of learning to choose from m discrete treatment opt...
08/31/2016 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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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...
09/17/2015 ∙ by Nathan Kallus, et al. ∙ 0 ∙ share

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Nathan Kallus

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