
Incentivizing Compliance with Algorithmic Instruments
Randomized experiments can be susceptible to selection bias due to poten...
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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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Knowledge Distillation as Semiparametric Inference
A popular approach to model compression is to train an inexpensive stude...
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Estimating the LongTerm Effects of Novel Treatments
Policy makers typically face the problem of wanting to estimate the long...
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EvidenceBased Policy Learning
The past years have seen seen the development and deployment of machine...
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Adversarial Estimation of Riesz Representers
We provide an adversarial approach to estimating Riesz representers of l...
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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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Bid Prediction in Repeated Auctions with Learning
We consider the problem of bid prediction in repeated auctions and evalu...
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Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional m...
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Double/Debiased Machine Learning for Dynamic Treatment Effects
We consider the estimation of treatment effects in settings when multipl...
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Simple, Credible, and ApproximatelyOptimal Auctions
We identify the first static credible mechanism for multiitem additive ...
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Dynamically Aggregating Diverse Information
An agent has access to multiple data sources, each of which provides inf...
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Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
We consider the estimation of heterogeneous treatment effects with arbit...
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SemiParametric Efficient Policy Learning with Continuous Actions
We consider offpolicy evaluation and optimization with continuous actio...
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Orthogonal Statistical Learning
We provide excess risk guarantees for statistical learning in the presen...
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NonParametric Inference Adaptive to Intrinsic Dimension
We consider nonparametric estimation and inference of conditional momen...
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Plugin Regularized Estimation of HighDimensional Parameters in Nonlinear Semiparametric Models
We develop a theory for estimation of a highdimensional sparse paramete...
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Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation
We study the problem of estimating heterogeneous treatment effects from ...
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Adversarial Generalized Method of Moments
We provide an approach for learning deep neural net representations of m...
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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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Semiparametric Contextual Bandits
This paper studies semiparametric contextual bandits, a generalization o...
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Lowrank Bandit Methods for Highdimensional Dynamic Pricing
We consider high dimensional dynamic multiproduct pricing with an evolv...
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Accurate Inference for Adaptive Linear Models
Estimators computed from adaptively collected data do not behave like th...
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Combinatorial Assortment Optimization
Assortment optimization refers to the problem of designing a slate of pr...
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Learning to Bid Without Knowing your Value
We address online learning in complex auction settings, such as sponsore...
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Optimal Data Acquisition for Statistical Estimation
We consider a data analyst's problem of purchasing data from strategic a...
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Orthogonal Machine Learning: Power and Limitations
Double machine learning provides √(n)consistent estimates of parameters...
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Training GANs with Optimism
We address the issue of limit cycling behavior in training Generative Ad...
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Inference on Auctions with Weak Assumptions on Information
Given a sample of bids from independent auctions, this paper examines th...
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Robust Optimization for NonConvex Objectives
We consider robust optimization problems, where the goal is to optimize ...
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A Proof of Orthogonal Double Machine Learning with ZEstimators
We consider two stage estimation with a nonparametric first stage and a...
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The Price of Anarchy in Auctions
This survey outlines a general and modular theory for proving approximat...
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Learning in Auctions: Regret is Hard, Envy is Easy
A line of recent work provides welfare guarantees of simple combinatoria...
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