The 2026 Program can be view at the bottom of this page.
PLENARY SESSION 1
MAY 12, 2026, 10:30AM – 12:00PM
Session Chair: Nandita Mitra
“Evidence on Trial: Causality in the Courtroom”
Cassy Stubbs
Cassy Stubbs directs the ACLU’s national Capital Punishment Project in its work toward repealing the death penalty through strategic litigation, training, and advocacy. With two decades of experience in capital representation, she has successfully represented client in trials, appeals, and post-conviction, securing victories in cases involving innocence, serious mental illness, competency, and intellectual disability. Cassy’s work has been instrumental in challenging systemic failings and racial discrimination in the administration of the death penalty. She represented each of the five defendants who succeeded at hearings under North Carolina’s Racial Justice Act, including a recent victory for Hasson Bacote in 2025. She has led challenges to the constitutionality of the death penalty in Kansas on behalf of four pre-trial defendants, resulting in a court order finding irredeemable faults with the death penalty. Under her leadership, the Capital Punishment Project has filed influential amicus briefs in state cases that have declared the death penalty unconstitutional.
Cassy is a frequent commentator on death penalty issues and has written and appeared in a host of media outlets. She regularly conducts capital defense trainings at conferences nationwide. Previously, she worked as a public defender in New Mexico, a workers’ rights attorney at Bet Tzedek Legal Services in Los Angeles, and a judicial law clerk to the Honorable Harry Pregerson of the U.S. Court of Appeals for the Ninth Circuit. Cassy is a graduate of New York University School of Law and Brown University. Based in Durham, NC, she is admitted to the bars of North Carolina, New York, and California.
Maria Cuellar
Maria Cuellar, PhD is an Assistant Professor at the University of Pennsylvania with joint appointments in the Department of Criminology and the Department of Statistics and Data Science, and a secondary appointment in Biostatistics, Epidemiology, and Informatics. Her research lies at the intersection of statistics and the law. She studies the statistical foundations of forensic science, focusing on the validity, reliability, and fairness of disciplines such as fingerprint, toolmark, and facial recognition analysis, and develops methods to quantify uncertainty in forensic conclusions. She also investigates how causal statements are made in legal contexts, particularly statements of attribution, using the probability of causation framework. Dr. Cuellar frequently serves as an expert witness in criminal trials and works to improve the scientific rigor of forensic evidence in the justice system.
Betsy Ogburn
Betsy Ogburn is Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. She is also member of the Data Science and AI Institute at Johns Hopkins University and affiliated faculty of the Center for Causal Inference at University of Pennsylvania. She works on causal inference, including interference and social networks, measurement error, semiparametric estimation, instrumental variables methods, and mediation analysis. Currently her main areas of research are unmeasured confounding and settings with statistical dependence. Betsy completed her Ph.D. in Biostatistics at Harvard University. She is a 2016 National Academy of Science Kavli Fellow and 2022 winner of the COPSS Emerging Leader Award.
PLENARY SESSION 2
MAY 13, 2026, 1:15PM-2:45PM
Session Chair: Fabrizia Mealli
“Foundations and Frontiers: The Econometric Roots of Causal Inference”
Whitney Newey
Whitney Newey is Ford Professor of Economics at MIT and a Research Associate of the National Bureau of Economic Research. He is a fellow of the Econometric Society, an elected member of the American Academy of Arts and Sciences, and distinguished fellow of the American Economic Association. He serves on the Executive Committee of the Econometric Society and served as chair of MIT Economics, co-editor of Econometrica, and program co-chair for the 2005 World Congress of the Econometric Society. Current research interests include debiased machine learning, panel data, and economic models with general heterogeneity.
Guido Imbens
Guido Imbens is The Applied Econometrics Professor at the Stanford Graduate School of Business and Professor of Economics in the Economics Department at Stanford University. He has held tenured positions at UCLA, UC Berkeley, and Harvard University before joining Stanford in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, “Causal Inference in Statistics, Social and Biomedical Sciences. Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, the National Academy of Sciences, and the American Statistical Association. He holds honorary doctorates from the University of St. Gallen, The University of Hull, Erasmus University Rotterdam, and Brown University. In 2017 he received the Horace Mann medal at Brown University. In 2021 he shared the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel with David Card and Joshua Angrist for “methodological contributions to the analysis of causal relationship.’’ Imbens was Editor of Econometrica from 2019-2025. Currently Imbens is also Director of Stanford Data Science.
Pedro Sant'anna
Pedro H. C. Sant’Anna is an Associate Professor of Economics at Emory University, specializing in applied econometrics and causal inference. His recent work focuses on advancing Difference-in-Differences methods, deepening their theoretical foundations and improving their practical implementation. He is a co-author of several widely used open-source packages that have shaped modern econometric practice. Pedro has published in leading economics journals and has guided the deployment of causal inference tools at major tech companies, bridging rigorous theory with real-world application at scale.
PLENARY SESSION 3
MAY 14, 2026, 10:30AM-12:00PM
Session Chair: Rohit Bhattacharya
“Are We Being Replaced? Causality in the Age of AI”
Ilya Shpitser
Ilya Shpitser is a John C. Malone Associate Professor of Computer Science at Johns Hopkins University. He works on all aspects of causal inference, including identification theory, semi-parametric theory, and model selection problems. In addition, he works on missing data, measurement error, systematic selection, dependent data, and other threats to statistical validity. The applications of his work are in public health and medicine.
Kun Zhang
Kun Zhang is a professor at Carnegie Mellon University (CMU), and he is also a visiting professor in the machine learning department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He has been advancing the machine learning perspective on causality for over 19 years, focusing on causal discovery from tabular data and causal representation learning from multimodal sources such as text, video, and images. His contributions address long-standing challenges, including uncovering causal structures with hidden variables, distinguishing cause from effect using distributional information, developing reliable nonparametric conditional independence tests, handling measurement error and missing data, and showing how causal perspectives can benefit generative AI. He has been frequently serving as a senior area chair, area chair, or senior program committee member for major conferences in machine learning or artificial intelligence, including UAI, NeurIPS, ICML, IJCAI, AISTATS, and ICLR. He was a general & program co-chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022), a program co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022) and International Conference on Data Mining (ICDM) 2024, and is a general co-chair of UAI 2023. He currently serves as an associate editor of JASA, JMLR, IEEE TPAMI, ACM Computing Surveys, etc.
Emre Kiciman
Emre Kıcıman is a Partner Research Manager at Microsoft and head of research at Copilot Tuning, where he leads efforts related to reasoning and model innovations and their applications to productivity scenarios. Emre’s research interests are broadly in large-scale applications of AI; AI’s impacts on people and society; and causal machine learning algorithms and generative AI systems. He is working to broaden the use of causal methods for decision-making across many application domains. Emre is a co-founder of the DoWhy causal inference library and the PyWhy open source ecosystem for causal AI.
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