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07/25/2023
Source Condition Double Robust Inference on Functionals of Inverse Problems
We consider estimation of parameters defined as linear functionals of so...
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03/08/2023
Inference on Optimal Dynamic Policies via Softmax Approximation
Estimating optimal dynamic policies from offline data is a fundamental p...
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02/17/2023
Post-Episodic Reinforcement Learning Inference
We consider estimation and inference with data collected from episodic r...
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02/10/2023
Minimax Instrumental Variable Regression and L_2 Convergence Guarantees without Identification or Closedness
In this paper, we study nonparametric estimation of instrumental variabl...
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11/03/2022
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation
We study the problem of model selection in causal inference, specificall...
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10/20/2022
Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime
We propose a generalization of the synthetic control and synthetic inter...
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10/14/2022
Partial Identification of Treatment Effects with Implicit Generative Models
We consider the problem of partial identification, the estimation of bou...
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06/03/2022
Debiased Machine Learning without Sample-Splitting for Stable Estimators
Estimation and inference on causal parameters is typically reduced to a ...
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04/10/2022
Towards efficient representation identification in supervised learning
Humans have a remarkable ability to disentangle complex sensory inputs (...
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12/26/2021
Omitted Variable Bias in Machine Learned Causal Models
We derive general, yet simple, sharp bounds on the size of the omitted v...
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10/06/2021
Robust Generalized Method of Moments: A Finite Sample Viewpoint
For many inference problems in statistics and econometrics, the unknown ...
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10/06/2021
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
Many causal and policy effects of interest are defined by linear functio...
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07/21/2021
Incentivizing Compliance with Algorithmic Instruments
Randomized experiments can be susceptible to selection bias due to poten...
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04/30/2021
Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression
We give debiased machine learners of parameters of interest that depend ...
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04/20/2021
Knowledge Distillation as Semiparametric Inference
A popular approach to model compression is to train an inexpensive stude...
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03/15/2021
Estimating the Long-Term Effects of Novel Treatments
Policy makers typically face the problem of wanting to estimate the long...
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03/12/2021
Evidence-Based Policy Learning
The past years have seen seen the development and deployment of machine-...
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12/30/2020
Adversarial Estimation of Riesz Representers
We provide an adversarial approach to estimating Riesz representers of l...
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11/23/2020
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality
One of the most commonly used methods for forming confidence intervals f...
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07/26/2020
Bid Prediction in Repeated Auctions with Learning
We consider the problem of bid prediction in repeated auctions and evalu...
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06/12/2020
Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional m...
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02/17/2020
Double/Debiased Machine Learning for Dynamic Treatment Effects
We consider the estimation of treatment effects in settings when multipl...
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02/16/2020
Simple, Credible, and Approximately-Optimal Auctions
We identify the first static credible mechanism for multi-item additive ...
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10/15/2019
Dynamically Aggregating Diverse Information
An agent has access to multiple data sources, each of which provides inf...
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05/24/2019
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
We consider the estimation of heterogeneous treatment effects with arbit...
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05/24/2019
Semi-Parametric Efficient Policy Learning with Continuous Actions
We consider off-policy evaluation and optimization with continuous actio...
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01/25/2019
Orthogonal Statistical Learning
We provide excess risk guarantees for statistical learning in the presen...
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01/11/2019
Non-Parametric Inference Adaptive to Intrinsic Dimension
We consider non-parametric estimation and inference of conditional momen...
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06/13/2018
Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models
We develop a theory for estimation of a high-dimensional sparse paramete...
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06/09/2018
Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation
We study the problem of estimating heterogeneous treatment effects from ...
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03/19/2018
Adversarial Generalized Method of Moments
We provide an approach for learning deep neural net representations of m...
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03/13/2018
Expert identification of visual primitives used by CNNs during mammogram classification
This work interprets the internal representations of deep neural network...
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03/12/2018
Semiparametric Contextual Bandits
This paper studies semiparametric contextual bandits, a generalization o...
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01/30/2018
Low-rank Bandit Methods for High-dimensional Dynamic Pricing
We consider high dimensional dynamic multi-product pricing with an evolv...
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12/18/2017
Accurate Inference for Adaptive Linear Models
Estimators computed from adaptively collected data do not behave like th...
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11/07/2017
Combinatorial Assortment Optimization
Assortment optimization refers to the problem of designing a slate of pr...
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11/03/2017
Learning to Bid Without Knowing your Value
We address online learning in complex auction settings, such as sponsore...
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11/03/2017
Optimal Data Acquisition for Statistical Estimation
We consider a data analyst's problem of purchasing data from strategic a...
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11/01/2017
Orthogonal Machine Learning: Power and Limitations
Double machine learning provides √(n)-consistent estimates of parameters...
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10/31/2017
Training GANs with Optimism
We address the issue of limit cycling behavior in training Generative Ad...
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10/10/2017
Inference on Auctions with Weak Assumptions on Information
Given a sample of bids from independent auctions, this paper examines th...
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07/04/2017
Robust Optimization for Non-Convex Objectives
We consider robust optimization problems, where the goal is to optimize ...
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04/12/2017
A Proof of Orthogonal Double Machine Learning with Z-Estimators
We consider two stage estimation with a non-parametric first stage and a...
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07/26/2016
The Price of Anarchy in Auctions
This survey outlines a general and modular theory for proving approximat...
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11/04/2015
