2023-24 Courses


Stanford Advisees


All Publications


  • Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations☆ JOURNAL OF ECONOMETRICS Athey, S., Imbens, G. W., Metzger, J., Munro, E. 2024; 240 (2)
  • Whitney Newey's contributions to econometrics JOURNAL OF ECONOMETRICS Abadie, A., Angrist, J., Imbens, G. 2024; 240 (2)
  • A New Look at P Values for Randomized Clinical Trials. NEJM evidence van Zwet, E., Gelman, A., Greenland, S., Imbens, G., Schwab, S., Goodman, S. N. 2024; 3 (1): EVIDoa2300003

    Abstract

    A New Look at P Values for Randomized Clinical TrialsUsing the primary results of 23,551 randomized clinical trials from the Cochrane Database, van Zwet et al. provide an empirical guide for the interpretation of an observed P value from a "typical" clinical trial in terms of the degree of overestimation of the reported effect, the probability of the effect's sign being wrong, and the predictive power of the trial.

    View details for DOI 10.1056/EVIDoa2300003

    View details for PubMedID 38320512

  • Optimal Experimental Design for Staggered Rollouts MANAGEMENT SCIENCE Xiong, R., Athey, S., Bayati, M., Imbens, G. 2023
  • A Design-Based Perspective on Synthetic Control Methods JOURNAL OF BUSINESS & ECONOMIC STATISTICS Bottmer, L., Imbens, G. W., Spiess, J., Warnick, M. 2023
  • Fixed Effects and the Generalized Mundlak Estimator REVIEW OF ECONOMIC STUDIES Arkhangelsky, D., Imbens, G. W. 2023
  • Experimental Design in Marketplaces STATISTICAL SCIENCE Bajari, P., Burdick, B., Imbens, G. W., Masoero, L., Mcqueen, J., Richardson, T. S., Rosen, I. M. 2023; 38 (3): 458-476

    View details for DOI 10.1214/23-STS883

    View details for Web of Science ID 001055135300005

  • Semi-parametric estimation of treatment effects in randomised experiments JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Athey, S., Bickel, P. J., Chen, A., Imbens, G. W., Pollmann, M. 2023
  • Causality in Econometrics: Choice vs Chance ECONOMETRICA Imbens, G. W. 2022; 90 (6): 2541-2566

    View details for DOI 10.3982/ECTA21204

    View details for Web of Science ID 000888233800003

  • When Should You Adjust Standard Errors for Clustering?* QUARTERLY JOURNAL OF ECONOMICS Abadie, A., Athey, S., Imbens, G. W., Wooldridge, J. M. 2022
  • Comment on: "Confidence Intervals for Nonparametric Empirical Bayes Analysis" by Ignatiadis and Wager JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Imbens, G. 2022; 117 (539): 1181-1182
  • Doubly robust identification for causal panel data models ECONOMETRICS JOURNAL Arkhangelsky, D., Imbens, G. W. 2022
  • Bayesian Meta-Prior Learning Using Empirical Bayes MANAGEMENT SCIENCE Nabi, S., Nassif, H., Hong, J., Mamani, H., Imbens, G. 2022; 68 (3): 1737-1755
  • Design-based analysis in Difference-In-Differences settings with staggered adoption JOURNAL OF ECONOMETRICS Athey, S., Imbens, G. W. 2022; 226 (1): 62-79
  • Synthetic Difference-in-Differences AMERICAN ECONOMIC REVIEW Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., Wager, S. 2021; 111 (12): 4088-4118
  • THE ET INTERVIEW: PROFESSOR GARY CHAMBERLAIN ECONOMETRIC THEORY Graham, B., Hirano, K., Imbens, G. 2021
  • Statistical Significance, p-Values, and the Reporting of Uncertainty JOURNAL OF ECONOMIC PERSPECTIVES Imbens, G. W. 2021; 35 (3): 157-173
  • A CAUSAL BOOTSTRAP ANNALS OF STATISTICS Imbens, G., Menzel, K. 2021; 49 (3): 1460-1488

    View details for DOI 10.1214/20-AOS2009

    View details for Web of Science ID 000684378300009

  • Matrix Completion Methods for Causal Panel Data Models JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Bayati, M., Doudchenko, N., Imbens, G., Khosravi, K. 2021
  • Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics JOURNAL OF ECONOMIC LITERATURE Imbens, G. W. 2020; 58 (4): 1129–79
  • Identification and Efficiency Bounds for the Average Match Function Under Conditionally Exogenous Matching JOURNAL OF BUSINESS & ECONOMIC STATISTICS Graham, B. S., Imbens, G. W., Ridder, G. 2020; 38 (2): 303–16
  • SAMPLING-BASED VERSUS DESIGN-BASED UNCERTAINTY IN REGRESSION ANALYSIS ECONOMETRICA Abadie, A., Athey, S., Imbens, G. W., Wooldridge, J. M. 2020; 88 (1): 265–96

    View details for DOI 10.3982/ECTA12675

    View details for Web of Science ID 000534143500008

  • Comment on: "The Blessings of Multiple Causes" by Yixin Wang and David M. Blei JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Imbens, G. W., Pollmann, M. 2019; 114 (528): 1602–4
  • Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs JOURNAL OF BUSINESS & ECONOMIC STATISTICS Gelman, A., Imbens, G. 2019; 37 (3): 447–56
  • Optimized Regression Discontinuity Designs REVIEW OF ECONOMICS AND STATISTICS Imbens, G., Wager, S. 2019; 101 (2): 264–78
  • External Validity in Fuzzy Regression Discontinuity Designs JOURNAL OF BUSINESS & ECONOMIC STATISTICS Bertanha, M., Imbens, G. W. 2019
  • Machine Learning Methods That Economists Should Know About ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019 Athey, S., Imbens, G. W., Aghion, P., Rey, H. 2019; 11: 685–725
  • Balanced Linear Contextual Bandits Dimakopoulou, M., Zhou, Z., Athey, S., Imbens, G., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 3445–53
  • Pharmacogenetic testing among patients with mood and anxiety disorders is associated with decreased utilization and cost: A propensity-score matched study DEPRESSION AND ANXIETY Perlis, R. H., Mehta, R., Edwards, A. M., Tiwari, A., Imbens, G. W. 2018; 35 (10): 946–52

    Abstract

    Naturalistic and small randomized trials have suggested that pharmacogenetic testing may improve treatment outcomes in depression, but its cost-effectiveness is not known. There is growing enthusiasm for personalized medicine, relying on genetic variation as a contributor to heterogeneity of treatment effects. We sought to examine the relationship between a commercial pharmacogenetic test for psychotropic medications and 6-month cost of care and utilization in a large commercial health plan.We performed a propensity-score matched case-control analysis of longitudinal health claims data from a large US insurer. Individuals with a mood or anxiety disorder diagnosis (N = 817) who received genetic testing for pharmacokinetic and pharmacodynamic variation were matched to 2,745 individuals who did not receive such testing. Outcomes included number of outpatient visits, inpatient hospitalizations, emergency room visits, and prescriptions, as well as associated costs over 6 months.On average, individuals who underwent testing experienced 40% fewer all-cause emergency room visits (mean difference 0.13 visits; P < 0.0001) and 58% fewer inpatient all-cause hospitalizations (mean difference 0.10 visits; P < 0.0001) than individuals in the control group. The two groups did not differ significantly in number of psychotropic medications prescribed or mood-disorder related hospitalizations. Overall 6-month costs were estimated to be $1,948 (SE 611) lower in the tested group.Pharmacogenetic testing represents a promising strategy to reduce costs and utilization among patients with mood and anxiety disorders.

    View details for PubMedID 29734486

  • Approximate residual balancing: debiased inference of average treatment effects in high dimensions JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Athey, S., Imbens, G. W., Wager, S. 2018; 80 (4): 597–623

    View details for DOI 10.1111/rssb.12268

    View details for Web of Science ID 000442217900001

  • Comments on understanding and misunderstanding randomized controlled trials: A commentary on Cartwright and Deaton. Social science & medicine (1982) Imbens, G. 2018

    View details for PubMedID 29735351

  • Addressing unmeasured confounding in comparative observational research PHARMACOEPIDEMIOLOGY AND DRUG SAFETY Zhang, X., Faries, D. E., Li, H., Stamey, J. D., Imbens, G. W. 2018; 27 (4): 373–82

    Abstract

    Observational pharmacoepidemiological studies can provide valuable information on the effectiveness or safety of interventions in the real world, but one major challenge is the existence of unmeasured confounder(s). While many analytical methods have been developed for dealing with this challenge, they appear under-utilized, perhaps due to the complexity and varied requirements for implementation. Thus, there is an unmet need to improve understanding the appropriate course of action to address unmeasured confounding under a variety of research scenarios.We implemented a stepwise search strategy to find articles discussing the assessment of unmeasured confounding in electronic literature databases. Identified publications were reviewed and characterized by the applicable research settings and information requirements required for implementing each method. We further used this information to develop a best practice recommendation to help guide the selection of appropriate analytical methods for assessing the potential impact of unmeasured confounding.Over 100 papers were reviewed, and 15 methods were identified. We used a flowchart to illustrate the best practice recommendation which was driven by 2 critical components: (1) availability of information on the unmeasured confounders; and (2) goals of the unmeasured confounding assessment. Key factors for implementation of each method were summarized in a checklist to provide further assistance to researchers for implementing these methods.When assessing comparative effectiveness or safety in observational research, the impact of unmeasured confounding should not be ignored. Instead, we suggest quantitatively evaluating the impact of unmeasured confounding and provided a best practice recommendation for selecting appropriate analytical methods.

    View details for PubMedID 29383840

  • Redefine statistical significance NATURE HUMAN BEHAVIOUR Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C., Cesarini, D., Chambers, C. D., Clyde, M., Cook, T. D., De Boeck, P., Dienes, Z., Dreber, A., Easwaran, K., Efferson, C., Fehr, E., Fidler, F., Field, A. P., Forster, M., George, E. I., Gonzalez, R., Goodman, S., Green, E., Green, D. P., Greenwald, A., Hadfield, J. D., Hedges, L. V., Held, L., Ho, T., Hoijtink, H., Hruschka, D. J., Imai, K., Imbens, G., Ioannidis, J. A., Jeon, M., Jones, J., Kirchler, M., Laibson, D., List, J., Little, R., Lupia, A., Machery, E., Maxwell, S. E., McCarthy, M., Moore, D., Morgan, S. L., Munafo, M., Nakagawa, S., Nyhan, B., Parker, T. H., Pericchi, L., Perugini, M., Rouder, J., Rousseau, J., Savalei, V., Schoenbrodt, F. D., Sellke, T., Sinclair, B., Tingley, D., Van Zandt, T., Vazire, S., Watts, D. J., Winship, C., Wolpert, R. L., Xie, Y., Young, C., Zinman, J., Johnson, V. E. 2018; 2 (1): 6–10
  • Redefine statistical significance. Nature human behaviour Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C., Cesarini, D., Chambers, C. D., Clyde, M., Cook, T. D., De Boeck, P., Dienes, Z., Dreber, A., Easwaran, K., Efferson, C., Fehr, E., Fidler, F., Field, A. P., Forster, M., George, E. I., Gonzalez, R., Goodman, S., Green, E., Green, D. P., Greenwald, A. G., Hadfield, J. D., Hedges, L. V., Held, L., Hua Ho, T., Hoijtink, H., Hruschka, D. J., Imai, K., Imbens, G., Ioannidis, J. P., Jeon, M., Jones, J. H., Kirchler, M., Laibson, D., List, J., Little, R., Lupia, A., Machery, E., Maxwell, S. E., McCarthy, M., Moore, D. A., Morgan, S. L., Munafó, M., Nakagawa, S., Nyhan, B., Parker, T. H., Pericchi, L., Perugini, M., Rouder, J., Rousseau, J., Savalei, V., Schönbrodt, F. D., Sellke, T., Sinclair, B., Tingley, D., Van Zandt, T., Vazire, S., Watts, D. J., Winship, C., Wolpert, R. L., Xie, Y., Young, C., Zinman, J., Johnson, V. E. 2018; 2 (1): 6-10

    View details for DOI 10.1038/s41562-017-0189-z

    View details for PubMedID 30980045

  • Exact p-Values for Network Interference JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Eckles, D., Imbens, G. W. 2018; 113 (521): 230–40
  • Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments BIOMETRICS Yang, S., Imbens, G. W., Cui, Z., Faries, D. E., Kadziola, Z. 2016; 72 (4): 1055-1065

    Abstract

    In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.

    View details for DOI 10.1111/biom.12505

    View details for Web of Science ID 000391932100005

    View details for PubMedID 26991040

  • Robust Standard Errors in Small Samples: Some Practical Advice REVIEW OF ECONOMICS AND STATISTICS Imbens, G. W., Kolesar, M. 2016; 98 (4): 701-712
  • Recursive partitioning for heterogeneous causal effects PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Athey, S., Imbens, G. 2016; 113 (27): 7353-7360

    Abstract

    In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without "sparsity" assumptions. We propose an "honest" approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the "ground truth" for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7-22%.

    View details for DOI 10.1073/pnas.1510489113

    View details for Web of Science ID 000379021700039

    View details for PubMedID 27382149

    View details for PubMedCentralID PMC4941430

  • Matching on the Estimated Propensity Score ECONOMETRICA Abadie, A., Imbens, G. W. 2016; 84 (2): 781-807

    View details for DOI 10.3982/ECTA11293

    View details for Web of Science ID 000373024100008

  • Identification and Inference With Many Invalid Instruments JOURNAL OF BUSINESS & ECONOMIC STATISTICS Kolesar, M., Chetty, R., Friedman, J., Glaeser, E., Imbens, G. W. 2015; 33 (4): 474-484
  • A Measure of Robustness to Misspecification AMERICAN ECONOMIC REVIEW Athey, S., Imbens, G. 2015; 105 (5): 476-480
  • Matching Methods in Practice Three Examples JOURNAL OF HUMAN RESOURCES Imbens, G. W. 2015; 50 (2): 373-419
  • Inference for Misspecified Models With Fixed Regressors JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Abadie, A., Imbens, G. W., Zheng, F. 2014; 109 (508): 1601-1614
  • Instrumental Variables: An Econometrician's Perspective STATISTICAL SCIENCE Imbens, G. W. 2014; 29 (3): 323-358

    View details for DOI 10.1214/14-STS480

    View details for Web of Science ID 000342603200001

  • Rejoinder STATISTICAL SCIENCE Imbens, G. 2014; 29 (3): 375-379

    View details for DOI 10.1214/14-STS496

    View details for Web of Science ID 000342603200006

  • Complementarity and aggregate implications of assortative matching: A nonparametric analysis QUANTITATIVE ECONOMICS Graham, B. S., Imbens, G. W., Ridder, G. 2014; 5 (1): 29-66

    View details for DOI 10.3982/QE45

    View details for Web of Science ID 000334344800002

  • Social Networks and the Identification of Peer Effects JOURNAL OF BUSINESS & ECONOMIC STATISTICS Goldsmith-Pinkham, P., Imbens, G. W. 2013; 31 (3): 253-264
  • Identification and inference in nonlinear difference-in-differences models ECONOMETRICA Athey, S., Imbens, G. W. 2006; 74 (2): 431-497