Search Results for author: Susan Athey

Found 49 papers, 23 papers with code

The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets

no code implementations13 Jul 2023 Susan Athey, Lisa K. Simon, Oskar N. Skans, Johan Vikstrom, Yaroslav Yakymovych

In contrast, workers in the least affected decile experience only marginal losses of less than 6 percent in the year after displacement.

Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization

no code implementations5 Jul 2023 Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill

We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees).

Multi-Armed Bandits

Federated Offline Policy Learning with Heterogeneous Observational Data

no code implementations21 May 2023 Aldo Gael Carranza, Susan Athey

We provide a novel regret analysis for our approach that establishes a finite-sample upper bound on a notion of global regret across a distribution of clients.

Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python

1 code implementation4 Apr 2023 Tianyu Du, Ayush Kanodia, Susan Athey

$\texttt{torch-choice}$ provides a $\texttt{ChoiceDataset}$ data structure to manage databases flexibly and memory-efficiently.

Synthetic Difference In Differences Estimation

no code implementations27 Jan 2023 Damian Clarke, Daniel Pailañir, Susan Athey, Guido Imbens

In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata.

Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning

no code implementations22 Nov 2022 Susan Athey, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams

We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses to determine which charity to expose them to in a donation solicitation.

Multi-Armed Bandits

Effective and scalable programs to facilitate labor market transitions for women in technology

no code implementations18 Nov 2022 Susan Athey, Emil Palikot

We find that optimal assignment increases participants' average probability of finding a job in technology by approximately 13% compared to random assignment.

Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

no code implementations2 Sep 2022 Susan Athey, Dean Karlan, Emil Palikot, Yuan Yuan

Online platforms often face challenges being both fair (i. e., non-discriminatory) and efficient (i. e., maximizing revenue).


Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial

no code implementations30 Aug 2022 Keshav Agrawal, Susan Athey, Ayush Kanodia, Emil Palikot

We study the impact of personalized content recommendations on the usage of an educational app for children.

Recommendation Systems

The Effectiveness of Digital Interventions on COVID-19 Attitudes and Beliefs

no code implementations21 Jun 2022 Susan Athey, Kristen Grabarz, Michael Luca, Nils Wernerfelt

During the course of the COVID-19 pandemic, a common strategy for public health organizations around the world has been to launch interventions via advertising campaigns on social media.

CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data

1 code implementation16 Feb 2022 Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei

We fit CAREER to a dataset of 24 million job sequences from resumes, and fine-tune its representations on longitudinal survey datasets.

Language Modelling Transfer Learning

Federated Causal Inference in Heterogeneous Observational Data

1 code implementation25 Jul 2021 Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, Susan Athey

We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site.

Causal Inference

Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles

no code implementations11 Jun 2021 Sanath Kumar Krishnamurthy, Susan Athey

We study the problem of model selection for contextual bandits, in which the algorithm must balance the bias-variance trade-off for model estimation while also balancing the exploration-exploitation trade-off.

Model Selection Multi-Armed Bandits

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits

1 code implementation3 Jun 2021 Ruohan Zhan, Vitor Hadad, David A. Hirshberg, Susan Athey

In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance.

Multi-Armed Bandits Off-policy evaluation

Policy Learning with Adaptively Collected Data

1 code implementation5 May 2021 Ruohan Zhan, Zhimei Ren, Susan Athey, Zhengyuan Zhou

Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education.

Multi-Armed Bandits

Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles

no code implementations26 Feb 2021 Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey

Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data.

Multi-Armed Bandits regression

Tractable contextual bandits beyond realizability

no code implementations25 Oct 2020 Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey

When realizability does not hold, our algorithm ensures the same guarantees on regret achieved by realizability-based algorithms under realizability, up to an additive term that accounts for the misspecification error.

Multi-Armed Bandits

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

no code implementations31 Jan 2020 Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li

Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data.


Optimal Experimental Design for Staggered Rollouts

1 code implementation9 Nov 2019 Ruoxuan Xiong, Susan Athey, Mohsen Bayati, Guido Imbens

Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected.

Decision Making Experimental Design

Confidence Intervals for Policy Evaluation in Adaptive Experiments

1 code implementation7 Nov 2019 Vitor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager, Susan Athey

In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero.

Experimental Design Multi-Armed Bandits

Using Wasserstein Generative Adversial Networks for the Design of Monte Carlo Simulations

2 code implementations5 Sep 2019 Susan Athey, Guido Imbens, Jonas Metzger, Evan Munro

We discuss the use of Wasserstein Generative Adversarial Networks (WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom.

Econometrics Methodology

Sufficient Representations for Categorical Variables

3 code implementations26 Aug 2019 Jonathan Johannemann, Vitor Hadad, Susan Athey, Stefan Wager

Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input.

Counterfactual Inference for Consumer Choice Across Many Product Categories

1 code implementation6 Jun 2019 Rob Donnelly, Francisco R. Ruiz, David Blei, Susan Athey

One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data.

Counterfactual Inference

Ensemble Methods for Causal Effects in Panel Data Settings

no code implementations24 Mar 2019 Susan Athey, Mohsen Bayati, Guido Imbens, Zhaonan Qu

This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment.

Matrix Completion regression

Machine Learning Methods Economists Should Know About

no code implementations24 Mar 2019 Susan Athey, Guido Imbens

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics.

BIG-bench Machine Learning Causal Inference +2

Estimating Treatment Effects with Causal Forests: An Application

2 code implementations20 Feb 2019 Susan Athey, Stefan Wager

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.


Synthetic Difference in Differences

4 code implementations24 Dec 2018 Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.


Balanced Linear Contextual Bandits

no code implementations15 Dec 2018 Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning.

Causal Inference Multi-Armed Bandits

Learning in Games with Lossy Feedback

no code implementations NeurIPS 2018 Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye

We consider a game-theoretical multi-agent learning problem where the feedback information can be lost during the learning process and rewards are given by a broad class of games known as variationally stable games.

Offline Multi-Action Policy Learning: Generalization and Optimization

1 code implementation10 Oct 2018 Zhengyuan Zhou, Susan Athey, Stefan Wager

In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action.

Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

no code implementations15 Aug 2018 Susan Athey, Guido Imbens

In this paper we study estimation of and inference for average treatment effects in a setting with panel data.

Local Linear Forests

3 code implementations30 Jul 2018 Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.

Causal Inference regression

Stable Prediction across Unknown Environments

no code implementations16 Jun 2018 Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li

In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments.

feature selection

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

no code implementations22 Jan 2018 Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt

The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants.

Variational Inference

Context Selection for Embedding Models

1 code implementation NeurIPS 2017 Liping Liu, Francisco Ruiz, Susan Athey, David Blei

Embedding models consider the probability of a target observation (a word or an item) conditioned on the elements in the context (other words or items).

Recommendation Systems Variational Inference +1

Estimation Considerations in Contextual Bandits

no code implementations19 Nov 2017 Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens

We develop parametric and non-parametric contextual bandits that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias.

Causal Inference Econometrics +1

Matrix Completion Methods for Causal Panel Data Models

2 code implementations27 Oct 2017 Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi

In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.

Statistics Theory Econometrics Statistics Theory

Structured Embedding Models for Grouped Data

1 code implementation NeurIPS 2017 Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei

Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data.

Word Embeddings

Sampling-based vs. Design-based Uncertainty in Regression Analysis

no code implementations6 Jun 2017 Alberto Abadie, Susan Athey, Guido W. Imbens, Jeffrey M. Wooldridge

We derive standard errors that account for design-based uncertainty instead of, or in addition to, sampling-based uncertainty.

Statistics Theory Econometrics Statistics Theory

Policy Learning with Observational Data

1 code implementation9 Feb 2017 Susan Athey, Stefan Wager

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints.


Generalized Random Forests

5 code implementations5 Oct 2016 Susan Athey, Julie Tibshirani, Stefan Wager

We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations.

Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions

1 code implementation25 Apr 2016 Susan Athey, Guido W. Imbens, Stefan Wager

There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables.

Methodology Econometrics Statistics Theory Statistics Theory

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index

no code implementations30 Mar 2016 Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang

We focus primarily on a setting with two samples, an experimental sample containing data about the treatment indicator and the surrogates and an observational sample containing information about the surrogates and the primary outcome.

Causal Inference

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

6 code implementations14 Oct 2015 Stefan Wager, Susan Athey

Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity.


Recursive Partitioning for Heterogeneous Causal Effects

1 code implementation5 Apr 2015 Susan Athey, Guido Imbens

The challenge is that the "ground truth" for a causal effect is not observed for any individual unit: we observe the unit with the treatment, or without the treatment, but not both at the same time.


Cannot find the paper you are looking for? You can Submit a new open access paper.