148 citations · 329 across the 21 of their papers we have counts for
22 papers
A Forecast Combination Framework for Hierarchical and Grouped Time Series Reconciliation
Xixi Li, Zijia Chen, James W. Taylor +1
Forecast combining and forecast reconciliation for hierarchical and grouped time series have largely developed as separate research areas. This paper connects the two by developing…
Statistical Properties of Robust Learning under Distributional Shifts
Zhiyi Li, Xiaojie Mao, Yunbei Xu +1
Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data. Robust learning frameworks such as Distribu…
Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
Jialu Xu, Mengkun Liang, Guannan Liu +2
Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the conditional average treatme…
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…
Source Condition Double Robust Inference on Functionals of Inverse Problems
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on…
Learning with Selectively Labeled Data from Multiple Decision-makers
Jian Chen, Zhehao Li, Xiaojie Mao
We study the problem of classification with selectively labeled data, whose distribution may differ from the full population due to historical decision-making. We exploit the fact…
Online Joint Assortment-Inventory Optimization under MNL Choices
Yong Liang, Xiaojie Mao, Shiyuan Wang
We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, 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…
Inference on Strongly Identified Functionals of Weakly Identified Functions
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data…
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Nathan Kallus, Xiaojie Mao, Kaiwen Wang +1
Off-policy evaluation and learning (OPE/L) use offline observational data to make better decisions, which is crucial in applications where online experimentation is limited. Howeve…
Long-term Causal Inference Under Persistent Confounding via Data Combination
Guido Imbens, Nathan Kallus, Xiaojie Mao +1
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only…
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…
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…
Stochastic Optimization Forests
Nathan Kallus, Xiaojie Mao
We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variab…
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…
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…
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…
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…
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao +2
Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabili…
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…
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Nathan Kallus, Xiaojie Mao, Madeleine Udell
Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased es…