148 citations · 972 across the 92 of their papers we have counts for
32 papers · 1 filter
SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
Brian Cho, Ana-Roxana Pop, Ariel Evnine +1
To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to de…
Adjusting Regression Models for Conditional Uncertainty Calibration
Ruijiang Gao, Mingzhang Yin, James McInerney +1
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be i…
The Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang, Nathan Kallus, Wen Sun
This paper illustrates the central role of loss functions in data-driven decision making, providing a comprehensive survey on their influence in cost-sensitive classification (CSC)…
Contextual Linear Optimization with Partial Feedback
Yichun Hu, Nathan Kallus, Xiaojie Mao +1
Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients in the objective and thereby improve decision-making perf…
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning
James McInerney, Nathan Kallus
Uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks. Existing methods quantify uncertainty at the last layer or other a…
Minimax Instrumental Variable Regression and Convergence Guarantees without Identification or Closedness
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. Recently, many flexible machine learning methods have been developed for instrumental va…
A Review of Off-Policy Evaluation in Reinforcement Learning
Masatoshi Uehara, Chengchun Shi, Nathan Kallus
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine learning and has been recently applied to solve a number of challenging problems. In this paper…
The Implicit Delta Method
Nathan Kallus, James McInerney
Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downst…
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
Nathan Kallus, Miruna Oprescu
The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) averag…
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara +1
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…
Fast Rates for Contextual Linear Optimization
Yichun Hu, Nathan Kallus, Xiaojie Mao
Incorporating side observations in decision making can reduce uncertainty and boost performance, but it also requires we tackle a potentially complex predictive relationship. While…
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret
Yichun Hu, Nathan Kallus
Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions both to an individual's initial features and to intermediate…
Comment: Entropy Learning for Dynamic Treatment Regimes
Nathan Kallus
I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Zeng (JSLZ) for an exciting development in conducting inferences on optimal dynamic treatment regimes (DTRs)…
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
Nathan Kallus, Xiaojie Mao
In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment…
Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus, Masatoshi Uehara
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the…
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient e…
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning
Nathan Kallus, Masatoshi Uehara
Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long- and infinite-horizon settings due to diminishing overlap between behavior and target policie…
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
Yichun Hu, Nathan Kallus, Xiaojie Mao
We study a nonparametric contextual bandit problem where the expected reward functions belong to a Hölder class with smoothness parameter . We show how this interpolates between…
Policy Evaluation with Latent Confounders via Optimal Balance
Andrew Bennett, Nathan Kallus
Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of n…
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Nathan Kallus, Angela Zhou
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual…
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus, Xiaojie Mao, Angela Zhou
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algor…
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard metho…
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from…
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Nathan Kallus, Xiaojie Mao, Angela Zhou
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to ind…