activity
20182021
most citedMachine Learning Methods Economists Should Know About

25 citations · 32 across the 4 of their papers we have counts for

collaborators

7 papers

stat.ME20214 cited

Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models

Guido Imbens, Nathan Kallus, Xiaojie Mao

We develop a new approach for identifying and estimating average causal effects in panel data under a linear factor model with unmeasured confounders. Compared to other methods tac…

econ.EM2019

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

Susan Athey, Guido Imbens, Jonas Metzger +1

When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credib…

econ.EM2019

Ensemble Methods for Causal Effects in Panel Data Settings

Susan Athey, Mohsen Bayati, Guido Imbens +1

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 t…

econ.EM201925 cited

Machine Learning Methods Economists Should Know About

Susan Athey, Guido Imbens

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the…

cs.LG20183 cited

Balanced Linear Contextual Bandits

Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey +1

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…

econ.EM2018

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

Susan Athey, Guido Imbens

In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or sta…