1.6k citations · 2.1k across the 53 of their papers we have counts for
22 papers · 1 filter
Long-Term Causal Inference with Many Noisy Proxies
Apoorva Lal, Guido Imbens, Peter Hull
We propose a method for estimating long-term treatment effects with many short-term proxy outcomes: a central challenge when experimenting on digital platforms. We formalize this c…
Estimating Variances for Causal Panel Data Estimators
Alexander Almeida, Susan Athey, Guido Imbens +2
There has been a recent surge in research on causal panel data models, leading to many new estimators for average causal effects. However, researchers have paid less attention to q…
Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
Xuelin Yang, Licong Lin, Susan Athey +2
We develop new methods to integrate experimental and observational data in causal inference. While randomized controlled trials offer strong internal validity, they are often costl…
Using Multiple Outcomes to Adjust Standard Errors for Spatial Correlation
Stefano DellaVigna, Guido Imbens, Woojin Kim +1
Empirical research in economics often examines the behavior of agents located in a geographic space. In such cases, statistical inference is complicated by the interdependence of e…
Identification of Average Treatment Effects in Nonparametric Panel Models
Susan Athey, Guido Imbens
This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatm…
PLRD: Partially Linear Regression Discontinuity Inference
Aditya Ghosh, Guido Imbens, Stefan Wager
Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confide…
Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?
Guido Imbens, Yiqing Xu
In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the tim…
Identification and Inference for Synthetic Controls with Confounding
Guido W. Imbens, Davide Viviano
This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and load…
Causal Models for Longitudinal and Panel Data: A Survey
Dmitry Arkhangelsky, Guido Imbens
In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with…
Causal clustering: design of cluster experiments under network interference
Davide Viviano, Lihua Lei, Guido Imbens +3
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering…
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Jann Spiess, Guido Imbens, Amar Venugopal
Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control…
Synthetic Difference In Differences Estimation
Damian Clarke, Daniel Pailañir, Susan Athey +1
In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-…
Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations
Xinkun Nie, Guido Imbens, Stefan Wager
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions. Such generaliza…
Design-Robust Two-Way-Fixed-Effects Regression For Panel Data
Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei +1
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-f…
A Design-Based Perspective on Synthetic Control Methods
Lea Bottmer, Guido Imbens, Jann Spiess +1
Since their introduction in Abadie and Gardeazabal (2003), Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observatio…
Optimal Experimental Design for Staggered Rollouts
Ruoxuan Xiong, Susan Athey, Mohsen Bayati +1
In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The…
Doubly Robust Identification for Causal Panel Data Models
Dmitry Arkhangelsky, Guido W. Imbens
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions…
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…
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…
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…
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…
Fixed Effects and the Generalized Mundlak Estimator
Dmitry Arkhangelsky, Guido Imbens
We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level…