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06/21/2023
Qini Curves for Multi-Armed Treatment Rules
Qini curves have emerged as an attractive and popular approach for evalu...
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04/23/2023
Policy Learning under Biased Sample Selection
Practitioners often use data from a randomized controlled trial to learn...
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02/22/2023
Experimenting under Stochastic Congestion
Stochastic congestion, a phenomenon in which a system becomes temporaril...
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09/05/2022
Learning from a Biased Sample
The empirical risk minimization approach to data-driven decision making ...
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09/01/2022
Switchback Experiments under Geometric Mixing
The switchback is an experimental design that measures treatment effects...
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07/15/2022
Treatment Heterogeneity for Survival Outcomes
Estimation of conditional average treatment effects (CATEs) plays an ess...
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04/04/2022
Policy Learning with Competing Agents
Decision makers often aim to learn a treatment assignment policy under a...
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03/02/2022
Partial Likelihood Thompson Sampling
We consider the problem of deciding how best to target and prioritize ex...
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02/24/2022
Thompson Sampling with Unrestricted Delays
We investigate properties of Thompson Sampling in the stochastic multi-a...
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02/10/2022
Network Interference in Micro-Randomized Trials
The micro-randomized trial (MRT) is an experimental design that can be u...
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12/09/2021
Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations
The ability to generalize experimental results from randomized control t...
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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...
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10/24/2021
Off-Policy Evaluation in Partially Observed Markov Decision Processes
We consider off-policy evaluation of dynamic treatment rules under the a...
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09/23/2021
Treatment Effects in Market Equilibrium
In evaluating social programs, it is important to measure treatment effe...
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04/08/2021
Average Treatment Effects in the Presence of Interference
We propose a definition for the average indirect effect of a binary trea...
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03/20/2021
Treatment Allocation under Uncertain Costs
We consider the problem of learning how to optimally allocate treatments...
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01/25/2021
Diffusion Asymptotics for Sequential Experiments
We propose a new diffusion-asymptotic analysis for sequentially randomiz...
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07/27/2020
Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
The network interference model for causal inference places all experimen...
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04/20/2020
Noise-Induced Randomization in Regression Discontinuity Designs
Regression discontinuity designs are used to estimate causal effects in ...
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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...
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11/07/2019
Confidence Intervals for Policy Evaluation in Adaptive Experiments
Adaptive experiments can result in considerable cost savings in multi-ar...
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10/23/2019
Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
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10/22/2019
Smoothness-Adaptive Stochastic Bandits
We consider the problem of non-parametric multi-armed bandits with stoch...
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09/25/2019
Cross-Validation, Risk Estimation, and Model Selection
Cross-validation is a popular non-parametric method for evaluating the a...
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08/26/2019
Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into...
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06/04/2019
Covariate-Powered Empirical Bayes Estimation
We study methods for simultaneous analysis of many noisy experiments in ...
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05/28/2019
Robust Nonparametric Difference-in-Differences Estimation
We consider the problem of treatment effect estimation in difference-in-...
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05/23/2019
Learning When-to-Treat Policies
Many applied decision-making problems have a dynamic component: The poli...
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05/02/2019
Sparsity Double Robust Inference of Average Treatment Effects
Many popular methods for building confidence intervals on causal effects...
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02/20/2019
Estimating Treatment Effects with Causal Forests: An Application
We apply causal forests to a dataset derived from the National Study of ...
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02/07/2019
Bias-Aware Confidence Intervals for Empirical Bayes Analysis
In an empirical Bayes analysis, we use data from repeated sampling to im...
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12/24/2018
Synthetic Difference in Differences
We present a new perspective on the Synthetic Control (SC) method as a w...
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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...
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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...
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07/30/2018
Local Linear Forests
Random forests are a powerful method for non-parametric regression, but ...
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12/13/2017
Learning Objectives for Treatment Effect Estimation
We develop a general class of two-step algorithms for heterogeneous trea...
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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...
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02/09/2017
Efficient Policy Learning
We consider the problem of using observational data to learn treatment a...
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10/05/2016
Generalized Random Forests
We propose generalized random forests, a method for non-parametric stati...
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07/22/2016
High-dimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experi...
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03/21/2016
Data Augmentation via Levy Processes
If a document is about travel, we may expect that short snippets of the ...
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10/14/2015
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Many scientific and engineering challenges -- ranging from personalized ...
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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...
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12/13/2014
The Statistics of Streaming Sparse Regression
We present a sparse analogue to stochastic gradient descent that is guar...
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10/30/2014
Bootstrap-Based Regularization for Low-Rank Matrix Estimation
We develop a flexible framework for low-rank matrix estimation that allo...
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07/11/2014
Altitude Training: Strong Bounds for Single-Layer Dropout
Dropout training, originally designed for deep neural networks, has been...
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05/02/2014
Asymptotic Theory for Random Forests
Random forests have proven to be reliable predictive algorithms in many ...
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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...
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10/10/2013
Feedback Detection for Live Predictors
A predictor that is deployed in a live production system may perturb the...
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10/04/2013