1.6k citations · 2.1k across the 53 of their papers we have counts for
61 papers
PICPIs: Prediction-Interval-Conditional Prediction Intervals
Xuelin Yang, Baihe Huang, Yilong Hou +2
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal prediction in nonparametric unce…
Estimating Causal Effects from Data Generated by Stochastic Algorithms
Susan Athey, Guido Imbens, Zoe Ji
Recommendation systems and chatbots present content to users, typically using stochastic algorithms that select the content based on user characteristics or context. Examples of co…
Regression Adjustments for Double Randomization in Two-Sided Marketplaces
Timothy Sudijono, Lihua Lei, Lorenzo Masoero +3
Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for…
Demonstration Experiments
Guido Imbens, Lorenzo Masoero, Alexander Rakhlin +2
Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatme…
Scalable Decisions using a Bayesian Decision-Theoretic Approach
Hoiyi Ng, Guido Imbens
Randomized controlled experiments assess new policy impacts on performance metrics to inform launch decisions. Traditional approaches evaluate metrics independently despite correla…
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…
Power Analysis is Essential: High-Powered Tests Suggest Minimal to No Effect of Rounded Shapes on Click-Through Rates
Ron Kohavi, Jakub Linowski, Lukas Vermeer +5
Underpowered studies (below 50% power) suffer from the winner's curse: A statistically significant positive estimate must exaggerate the true treatment effect to meet the significa…
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…
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
Carlos Cinelli, Avi Feller, Guido Imbens +3
Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer scienc…
Triply Robust Panel Estimators
Susan Athey, Guido Imbens, Zhaonan Qu +1
This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply RObust Panel (TROP) estimator, that combines (i) a flexible model…
Causal Inference when Intervention Units and Outcome Units Differ
Georgia Papadogeorgou, Zhaoyan Song, Guido Imbens +1
We study causal inference in settings characterized by interference with a bipartite structure. There are two distinct sets of units: intervention units to which an intervention ca…
Robust and efficient multiple-unit switchback experimentation
Paul Missault, Lorenzo Masoero, Christian Delbé +2
User-randomized A/B testing has emerged as the gold standard for online experimentation. However, when this kind of approach is not feasible due to legal, ethical or practical cons…
Admissibility of Completely Randomized Trials: A Large-Deviation Approach
Guido Imbens, Chao Qin, Stefan Wager
When an experimenter has the option of running an adaptive trial, is it admissible to ignore this option and run a non-adaptive trial instead? We provide a negative answer to this…
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…
Neymanian inference in randomized experiments
Ambarish Chattopadhyay, Guido W. Imbens
In his seminal 1923 work, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments.…
Computationally Efficient Estimation of Large Probit Models
Patrick Ding, Guido Imbens, Zhaonan Qu +1
Probit models are useful for modeling correlated discrete responses in many disciplines, including consumer choice data in economics and marketing. However, the Gaussian latent var…
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…
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limit…
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…