About

Guido W Imbens is an Applied Econometrics Professor at the Graduate School of Business and a Professor of Economics at the School of Humanities & Sciences at Stanford University. He is also a Senior Fellow at the Stanford Institute for Economic Policy Research and a member of the Academic Council. His research focuses on developing methods for drawing causal inferences in observational studies, utilizing techniques such as matching, instrumental variables, and regression discontinuity designs. Imbens has been recognized for his significant contributions to econometrics and statistics, notably being awarded the 2021 Nobel Sveriges Riksbank Prize in Economic Sciences for his work in these areas. He is a distinguished member of the academic community, with a notable record of research achievements and honors.

Research topics

• Econometrics
• Computer science
• Statistics
• Mathematics
• Economics

Selected publications

• ADIA Lab Causal Discovery Challenge

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• ADIA Lab Causal Discovery Challenge

  SSRN Electronic Journal · 2026-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades after LaLonde (1986)?

  The Journal of Economic Perspectives · 2025-11-01 · 3 citations

  articleOpen access1st authorCorresponding

  In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (1) an emphasis on the unconfoundedness assumption separated from functional form considerations, (2) a focus on the importance of overlap in covariate distributions, (3) the introduction of propensity score-based methods leading to doubly robust estimators, (4) methods for estimating and exploiting treatment effect heterogeneity, and (5) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde data. We show that modern methods, when applied in contexts with sufficient covariate overlap, yield robust estimates for the adjusted differences between the treatment and control groups. However, this does not imply that these estimates are causally interpretable. To assess their credibility, validation exercises (such as placebo tests) are essential, whereas goodness-of-fit tests alone are inadequate. Our findings highlight the importance of closely examining the assignment process, carefully inspecting overlap, and conducting validation exercises when analyzing causal effects with nonexperimental data.

  PublisherOA PDFDOI

• Admissibility of Completely Randomized Trials: A Large-Deviation Approach

  2025-07-02

  articleOpen access1st authorCorresponding

  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 question in the best-arm identification problem, where the experimenter aims to allocate measurement efforts judiciously to confidently deploy the most effective treatment arm. We find that, whenever there are at least three treatment arms, there exist simple adaptive designs that universally and strictly dominate non-adaptive completely randomized trials. This dominance is characterized by a notion called efficiency exponent, which quantifies a design's statistical efficiency when the experimental sample is large. Our analysis focuses on the class of batched arm elimination designs, which progressively eliminate underperforming arms at pre-specified batch intervals. We characterize simple sufficient conditions under which these designs universally and strictly dominate completely randomized trials. These results resolve the second open problem posed in Qin [2022]. The full version of this paper is available at https://arxiv.org/pdf/2506.05329.

  PublisherOA PDFDOI

• Replication Data for: Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?

  ICPSR Data Holdings · 2025-01-01

  datasetOpen access1st authorCorresponding

  In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form considerations, (ii) a focus on the importance of overlap in covariate distributions, (iii) the introduction of propensity score-based methods leading to doubly robust estimators, (iv) methods for estimating and exploiting treatment effect heterogeneity, and (v) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde data. We show that modern methods, when applied in contexts with sufficient covariate overlap, yield robust estimates for the adjusted differences between the treatment and control groups. However, this does not imply that these estimates are causally interpretable. To assess their credibility, validation exercises (such as placebo tests) are essential, whereas goodness-of-fit tests alone are inadequate. Our findings highlight the importance of closely examining the assignment process, carefully inspecting overlap, and conducting validation exercises when analyzing causal effects with nonexperimental data.

  PublisherDOI

• Redefine statistical significance

  Artefactual Field Experiments · 2025-01-10 · 21 citations

  articleOpen access
  PublisherOA PDFDOI

• PLRD: Partially Linear Regression Discontinuity Inference

  ArXiv.org · 2025-03-12

  preprintOpen access

  Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions.

  PublisherOA PDFDOI

• The Experimental Selection Correction Estimator: Using Experiments to Remove Biases in Observational Estimates

  SSRN Electronic Journal · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• An Undergraduate Course in Causality

  Harvard Data Science Review · 2025-10-29

  articleOpen access

  In the Fall quarter of 2024 we (a computer scientist and an economist as the faculty in charge of the course, with two economics graduate students as course assistants) taught an undergraduate course with the title “Causality, Decision Making, and Data Science,” cross-listed in the Economics Department, the Data Science Major, the Computer Science Department and the Graduate School of Business undergraduate program. The course was primarily intended for freshmen and sophomores, but because it was the first time we offered it, we also admitted juniors and a few seniors. We restricted enrollment to forty students to make the course interactive. The course was case-based, with minimal statistics requirements. It was successful from our perspective, and student evaluations reflected a similarly positive view. We would like to share here some of what we learned. The materials we put together, including an extensive set of slides, problem sets, and data sets, are available on this website <https://stanford-causalinference-class.github.io/> (https://stanford-causalinference-class.github.io/ <https://stanford-causalinference-class.github.io/> ).

  PublisherOA PDFDOI

• Estimating Variances for Causal Panel Data Estimators

  ArXiv.org · 2025-10-13

  preprintOpen access

  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 quantifying the precision of these estimators. This paper addresses that gap by studying the problem of variance estimation in causal panel settings. We develop a unified framework for comparing the three main variance estimators used in these settings: regression-based, Unit-Placebo, and Time-Placebo estimators. We show that each relies on a distinct exchangeability assumption and, correspondingly, each targets a different conditional variance. We find that, under some assumptions, all three estimators are all valid, but that their statistical power differs substantially depending on the heteroskedasticity present in the data. Building on these insights, we propose a new variance estimator that flexibly accounts for heteroskedasticity across the unit and time dimensions, and delivers superior statistical power in realistic panel data settings.

  PublisherOA PDFDOI

Recent grants

• `Collaborative Research: Estimation for and Inference on Causal Effects

  NSF · $135k · 2006–2010

Frequent coauthors

• Susan Athey

  222 shared

• Alberto Abadie

  100 shared

• Donald B. Rubin

  Temple University

  79 shared

• Geert Ridder

  59 shared

• Joshua D. Angrist

  58 shared

• Jeffrey M. Wooldridge

  Michigan State University

  52 shared

• Dmitry Arkhangelsky

  48 shared

• Keisuke Hirano

  Toyohashi Heart Center

  45 shared

Awards & honors

• Member of the National Academy of Sciences
• Nobel Sveriges Riksbank Prize in Economic Sciences (2021)