b'Stefan Wager'

research

∙ 06/21/2023

Qini Curves for Multi-Armed Treatment Rules

Qini curves have emerged as an attractive and popular approach for evalu...

0 Erik Sverdrup, et al. ∙

research

∙ 04/23/2023

Policy Learning under Biased Sample Selection

Practitioners often use data from a randomized controlled trial to learn...

0 Lihua Lei, et al. ∙

research

∙ 02/22/2023

Experimenting under Stochastic Congestion

Stochastic congestion, a phenomenon in which a system becomes temporaril...

0 Shuangning Li, et al. ∙

research

∙ 09/05/2022

Learning from a Biased Sample

The empirical risk minimization approach to data-driven decision making ...

1 Roshni Sahoo, et al. ∙

research

∙ 09/01/2022

Switchback Experiments under Geometric Mixing

The switchback is an experimental design that measures treatment effects...

0 Yuchen Hu, et al. ∙

research

∙ 07/15/2022

Treatment Heterogeneity for Survival Outcomes

Estimation of conditional average treatment effects (CATEs) plays an ess...

0 Yizhe Xu, et al. ∙

research

∙ 04/04/2022

Policy Learning with Competing Agents

Decision makers often aim to learn a treatment assignment policy under a...

0 Roshni Sahoo, et al. ∙

research

∙ 03/02/2022

Partial Likelihood Thompson Sampling

We consider the problem of deciding how best to target and prioritize ex...

0 Han Wu, et al. ∙

research

∙ 02/24/2022

Thompson Sampling with Unrestricted Delays

We investigate properties of Thompson Sampling in the stochastic multi-a...

0 Han Wu, et al. ∙

research

∙ 02/10/2022

Network Interference in Micro-Randomized Trials

The micro-randomized trial (MRT) is an experimental design that can be u...

0 Shuangning Li, et al. ∙

research

∙ 12/09/2021

Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations

The ability to generalize experimental results from randomized control t...

0 Xinkun Nie, et al. ∙

research

∙ 11/15/2021

Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

There are a number of available methods that can be used for choosing wh...

0 Steve Yadlowsky, et al. ∙

research

∙ 10/24/2021

Off-Policy Evaluation in Partially Observed Markov Decision Processes

We consider off-policy evaluation of dynamic treatment rules under the a...

0 Yuchen Hu, et al. ∙

research

∙ 09/23/2021

Treatment Effects in Market Equilibrium

In evaluating social programs, it is important to measure treatment effe...

0 Evan Munro, et al. ∙

research

∙ 04/08/2021

Average Treatment Effects in the Presence of Interference

We propose a definition for the average indirect effect of a binary trea...

0 Yuchen Hu, et al. ∙

research

∙ 03/20/2021

Treatment Allocation under Uncertain Costs

We consider the problem of learning how to optimally allocate treatments...

0 Hao Sun, et al. ∙

research

∙ 01/25/2021

Diffusion Asymptotics for Sequential Experiments

We propose a new diffusion-asymptotic analysis for sequentially randomiz...

0 Stefan Wager, et al. ∙

research

∙ 07/27/2020

Random Graph Asymptotics for Treatment Effect Estimation under Network Interference

The network interference model for causal inference places all experimen...

0 Shuangning Li, et al. ∙

research

∙ 04/20/2020

Noise-Induced Randomization in Regression Discontinuity Designs

Regression discontinuity designs are used to estimate causal effects in ...

0 Dean Eckles, et al. ∙

research

∙ 01/27/2020

Estimating heterogeneous treatment effects with right-censored data via causal survival forests

There is fast-growing literature on estimating heterogeneous treatment e...

1 Yifan Cui, et al. ∙

research

∙ 11/07/2019

Confidence Intervals for Policy Evaluation in Adaptive Experiments

Adaptive experiments can result in considerable cost savings in multi-ar...

14 Vitor Hadad, et al. ∙

research

∙ 10/23/2019

Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in mo...

0 Imke Mayer, et al. ∙

research

∙ 10/22/2019

Smoothness-Adaptive Stochastic Bandits

We consider the problem of non-parametric multi-armed bandits with stoch...

0 Yonatan Gur, et al. ∙

research

∙ 09/25/2019

Cross-Validation, Risk Estimation, and Model Selection

Cross-validation is a popular non-parametric method for evaluating the a...

0 Stefan Wager, et al. ∙

research

∙ 08/26/2019

Sufficient Representations for Categorical Variables

Many learning algorithms require categorical data to be transformed into...

25 Jonathan Johannemann, et al. ∙

research

∙ 06/04/2019

Covariate-Powered Empirical Bayes Estimation

We study methods for simultaneous analysis of many noisy experiments in ...

0 Nikolaos Ignatiadis, et al. ∙

research

∙ 05/28/2019

Robust Nonparametric Difference-in-Differences Estimation

We consider the problem of treatment effect estimation in difference-in-...

0 Chen Lu, et al. ∙

research

∙ 05/23/2019

Learning When-to-Treat Policies

Many applied decision-making problems have a dynamic component: The poli...

0 Xinkun Nie, et al. ∙

research

∙ 05/02/2019

Sparsity Double Robust Inference of Average Treatment Effects

Many popular methods for building confidence intervals on causal effects...

0 Jelena Bradic, et al. ∙

research

∙ 02/20/2019

Estimating Treatment Effects with Causal Forests: An Application

We apply causal forests to a dataset derived from the National Study of ...

0 Susan Athey, et al. ∙

research

∙ 02/07/2019

Bias-Aware Confidence Intervals for Empirical Bayes Analysis

In an empirical Bayes analysis, we use data from repeated sampling to im...

0 Nikolaos Ignatiadis, et al. ∙

research

∙ 12/24/2018

Synthetic Difference in Differences

We present a new perspective on the Synthetic Control (SC) method as a w...

0 Dmitry Arkhangelsky, et al. ∙

research

∙ 11/06/2018

Debiased Inference of Average Partial Effects in Single-Index Models

We propose a method for average partial effect estimation in high-dimens...

0 David A. Hirshberg, et al. ∙

research

∙ 10/10/2018

Offline Multi-Action Policy Learning: Generalization and Optimization

In many settings, a decision-maker wishes to learn a rule, or policy, th...

4 Zhengyuan Zhou, et al. ∙

research

∙ 07/30/2018

Local Linear Forests

Random forests are a powerful method for non-parametric regression, but ...

52 Rina Friedberg, et al. ∙

research

∙ 12/13/2017

Learning Objectives for Treatment Effect Estimation

We develop a general class of two-step algorithms for heterogeneous trea...

0 Xinkun Nie, et al. ∙

research

∙ 11/30/2017

Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities

There has been a recent surge of interest in doubly robust approaches to...

0 David A. Hirshberg, et al. ∙

research

∙ 02/09/2017

Efficient Policy Learning

We consider the problem of using observational data to learn treatment a...

0 Susan Athey, et al. ∙

research

∙ 10/05/2016

Generalized Random Forests

We propose generalized random forests, a method for non-parametric stati...

0 Susan Athey, et al. ∙

research

∙ 07/22/2016

High-dimensional regression adjustments in randomized experiments

We study the problem of treatment effect estimation in randomized experi...

0 Stefan Wager, et al. ∙

research

∙ 03/21/2016

Data Augmentation via Levy Processes

If a document is about travel, we may expect that short snippets of the ...

0 Stefan Wager, et al. ∙

research

∙ 10/14/2015

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Many scientific and engineering challenges -- ranging from personalized ...

0 Stefan Wager, et al. ∙

research

∙ 03/22/2015

Adaptive Concentration of Regression Trees, with Application to Random Forests

We study the convergence of the predictive surface of regression trees a...

0 Stefan Wager, et al. ∙

research

∙ 12/13/2014

The Statistics of Streaming Sparse Regression

We present a sparse analogue to stochastic gradient descent that is guar...

0 Jacob Steinhardt, et al. ∙

research

∙ 10/30/2014

Bootstrap-Based Regularization for Low-Rank Matrix Estimation

We develop a flexible framework for low-rank matrix estimation that allo...

0 Julie Josse, et al. ∙

research

∙ 07/11/2014

Altitude Training: Strong Bounds for Single-Layer Dropout

Dropout training, originally designed for deep neural networks, has been...

0 Stefan Wager, et al. ∙

research

∙ 05/02/2014

Asymptotic Theory for Random Forests

Random forests have proven to be reliable predictive algorithms in many ...

0 Stefan Wager, et al. ∙

research

∙ 11/18/2013

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

We study the variability of predictions made by bagged learners and rand...

0 Stefan Wager, et al. ∙

research

∙ 10/10/2013

Feedback Detection for Live Predictors

A predictor that is deployed in a live production system may perturb the...

0 Stefan Wager, et al. ∙

research

∙ 10/04/2013

Weakly supervised clustering: Learning fine-grained signals from coarse labels

Consider a classification problem where we do not have access to labels ...

0 Stefan Wager, et al. ∙

Stefan Wager

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