CDSM 2026 / Program & Speakers

Program &
Speakers 2026.

November 4–5, 2026Online

Two days of talks and discussion on causal data science, machine learning and AI. Free to attend.

November 4, 2026

November 4

Session 01

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  • Randomised Treatment, Incomplete Measurement: Can We Trust an Experiment in Advertising?

    Bugaev, A. (Bolt)

  • Object-Centric Causal Discovery in MIMIC-IV: A Discovery-to-Inference Workflow for Identifying Candidate Upstream Drivers of Hospital-Acquired Pressure Injury

    Molitor, M. (Xplain GmbH); Haft, M. (Xplain Data); Alderden, J. (Boise State University); Wilson, A.* (University of Utah; Tao of RWD)

  • Causal Graphs for Conditional Parallel Trends

    Knaus, M. C. (University of Tübingen); Pfleiderer, H. (University of Tübingen)

  • Causal AI in Industry: From Theory to Real-World Adoption

    Patel, V. (Insytes GmbH)

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Break

November 4

Session 02

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  • Counterfactual Optimization of Policy Interventions: Lexical Ordering and Leapfrogging

    Freidling, T. (EPFL); Zhao, Q. (University of Cambridge); Scauda, M. (University of Cambridge)

  • Do Gender Quotas Harm Firms? Theory and Evidence from European Corporate Boards

    Huebler, M. (Oesterreichische Nationalbank); Sigmund, M. (Oesterreichische Nationalbank)

  • From DAG to Decision: pathmc, a Bayesian Structural Causal Modelling Toolkit for Practitioners

    Vincent, B. (PyMC Labs)

  • From Causal Evidence to CAPA: A Framework for Causal Decision Support in Regulated Medical-Device Manufacturing

    Jervis, J. (Boston Scientific)

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Break

November 4

Session 03

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  • Graph Preconditioning for High-Dimensional Fixed Effects Regression

    Schröder, K. (appliedAI Institute for Europe); Fischer, A. (trivago)

  • Causal Inference and Model Explainability Tools for Retail

    Surendran, N. (Lowe’s Companies Inc.); Gupta, P. (Lowe’s Companies Inc.)

  • Adaptive Elicitation of Heterogeneous Causal Knowledge for Efficient Causal Discovery

    Inan, E. (University of Toronto); Shrestha, R. B. (Max Planck Institute for Intelligent Systems); Zhang, L. (University of Toronto); Kong, D. (University of Toronto); Jin, Z. (EuroSafeAI); Qi, Y. (University of Toronto)

  • Wasserstein Causal Forests for Distribution-Valued Outcomes

    Gobato Souto, H. (University of São Paulo)

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Break

November 4

Session 04

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  • A Holdout-Anchored Calibration Bound on Short-Run Experiment Arms for Proxy-Metric Development

    Karako, C. (Shopify); Madduri, V. (Shopify); Kahn, J. (Shopify)

  • Causal Discovery via Simultaneous DAG Recovery Using the Angles Space of Directional Dependence Measures

    Opdyke, J. D. (DataMineit, LLC)

  • The Explanatory Power of Causal Effects

    Thor, N. (Brown University); Stratton, J. (Harvard University)

  • Fixed Effects and Collider Bias: When Absorbing Group Heterogeneity Creates Endogeneity

    Burkhardt, J. (Colorado State); Jung, J. (Colorado State); Hünermund, P. (Technical University of Munich)

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Break

November 4

Debate

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In Debate.

Nick Huntington-Klein and Vasilis Syrgkanis debate the role of agentic AI in causal inference.

Nick Huntington-Klein

Seattle University

Associate Professor of Economics. Works on econometrics, causal inference and higher education policy; author of The Effect: An Introduction to Research Design and Causality.

Website
Vasilis Syrgkanis

Stanford University

Assistant Professor of Management Science and Engineering and lead of the Stanford Causal AI Lab. Previously Principal Researcher at Microsoft Research New England and co-founder of EconML.

Website
Add the Debate to Your Calendar

November 5, 2026

November 5

Session 01

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  • Testing Alternative Identifying Assumptions in Panel Data with Pre-Treatment Periods

    Huber, M. (University of Fribourg); Oeß, E.-M. (University of Cologne)

  • Foundation Models for Causal Effect Estimation and Conditional Discovery in Marketing

    Säilynoja, T. (PyMC Labs); Trujillo, C. (PyMC Labs)

  • Dirichlet-Tree Allocation Priors for Bayesian Causal Forests

    Klippert, D. (Private); Balci, Y. (Amsterdam UMC); Marx, A. (Private); Aguilar, J. E. (TU Dortmund)

  • Double Machine Learning with High-Dimensional Interactive Fixed Effects

    Polselli, A. (University of Essex); Clarke, P. S. (University of Essex); Chen, B. (University of Essex)

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Break

November 5

Session 02

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  • Better Business Decisions through a New Causal Perspective

    Dul, J. (Rotterdam School of Management, Erasmus University); Haan, G. (Geraken Consultancy)

  • Automatic Debiased Machine Learning and Sensitivity Analysis for Sample Selection Models

    Bjelac, J. (Private); Chernozhukov, V. (MIT); Klotz, P.-A. (Heinrich Heine University Düsseldorf); Kück, J. (Heinrich Heine University Düsseldorf); Schmitz, T. M. A. (Heinrich Heine University Düsseldorf)

  • Building the Mechanism: Bivalent, Exhaustive Mediators for Front-Door Identification with Bayesian SEMs

    Forde, N. (Pandadoc)

  • The Bright Side of Circuit Breakers

    Renò, R. (ESSEC Business School); Ventura, M. (University of Cergy; Ca’ Foscari University of Venice)

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Break

November 5

Spotlight Session

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  • CauSciBench: Can LLMs Automate Causal Inference in Real-World Scientific Research?

    Acharya, S., Zhang, T. J., Shrestha, R. B. et al.

    All 20 Authors & Affiliations
    • Acharya, S. (University of Toronto & Vector Institute; Stanford University)
    • Zhang, T. J. (University of Toronto & Vector Institute; ETH Zürich; EuroSafeAI)
    • Shrestha, R. B. (University of Toronto & Vector Institute)
    • Sun, X. (HKU)
    • Cobben, P. (University of Toronto & Vector Institute; ETH Zürich; EuroSafeAI)
    • Mordig, M. (ETH Zürich; Max Planck Institute for Intelligent Systems, Tübingen)
    • Emmerson, J. T. (University of Toronto & Vector Institute)
    • Haghighat, A. (University of Toronto & Vector Institute)
    • Danisman, F. (University of Toronto & Vector Institute)
    • Chen, Y. (UIUC)
    • Jose, C. (IP Paris)
    • Muresanu, A. I. (University of Toronto & Vector Institute; EuroSafeAI)
    • Cui, J. (University of Toronto & Vector Institute)
    • Liu, J. (University of Toronto & Vector Institute; CMU)
    • Qi, Y. (University of Toronto & Vector Institute)
    • Pandey, P. S. (University of Toronto & Vector Institute; EuroSafeAI)
    • Huang, Y. (ETH Zürich)
    • Schölkopf, B. (Max Planck Institute for Intelligent Systems, Tübingen; ELLIS Institute Tübingen)
    • Jin, Z. (University of Toronto & Vector Institute; EuroSafeAI; Max Planck Institute for Intelligent Systems, Tübingen)
    • Kim, A. (University of Toronto & Vector Institute)

  • Causal-Graph-Guided Reinforcement Learning for LLM Security Testing: An Empirical Study

    Zeinaliashtiyani, P. (University of Naples Federico II)

  • Inferring Failure Processes via Causality Analysis: From Event Logs to Predictive Fault Trees

    Depaire, B. (Hasselt University); Marrone, S. (University of Campania “Luigi Vanvitelli”); Verde, L. (University of Campania “Luigi Vanvitelli”); De Fazio, R. (University of Campania “Luigi Vanvitelli”)

  • Inference on Multiple Treatment Effects with an Application to Health Economics

    Chen, C. Y.-H. (University of Glasgow); Chernozhukov, V. (MIT); Spindler, M. (University of Hamburg); Rabenseifner, J. (University of Hamburg)

  • Testing Instrument Validity in Peer-Effects Models

    Huber, M. (University of Fribourg); Kück, J. (Heinrich Heine University Düsseldorf); Schmidt, A. (Heinrich Heine University Düsseldorf); Lafférs, L. (Institute of Economic Research, Slovak Academy of Sciences)

  • KOSKINO: A Design-Stage Framework for Causal Evaluation Without Explicit Controls

    Tsoumas, I., Bormpoudakis, D., Tsardanidis, I. et al.

    All 8 Authors & Affiliations
    • Tsoumas, I.* (National Observatory of Athens; Wageningen University & Research)
    • Bormpoudakis, D. (National Observatory of Athens; Harokopio University)
    • Tsardanidis, I. (National Observatory of Athens)
    • Askitopoulos, A. (National Observatory of Athens)
    • Kontoes, C. (National Observatory of Athens)
    • Camps-Valls, G. (Universitat de València)
    • Athanasiadis, I. N. (Wageningen University & Research)
    • Sitokonstantinou, V. (Wageningen University & Research)

  • Optimal Causal Representations: Discovering Categorical Causal Estimands from Text Data

    Matseliukh, D. (University of Kassel); Giordano, V. (University of Pisa); Arroyo Portillo, J. (University of Kassel)

  • Causal AI Scientist: Towards End-to-End Causal Inference with Large Language Models

    Acharya, S., Emmerson, J. T. … Shrestha, R. B.

    All 13 Authors & Affiliations
    • Acharya, S. (University of Toronto & Vector Institute; Stanford University)
    • Emmerson, J. T. (University of Toronto & Vector Institute)
    • Verma, V. (University of Toronto & Vector Institute)
    • Bhardwaj, D. (University of Toronto & Vector Institute)
    • Simko, S. (University of Toronto & Vector Institute; EuroSafeAI)
    • Pandey, P. S. (University of Toronto & Vector Institute; EuroSafeAI)
    • Haghighat, A. (University of Toronto & Vector Institute)
    • Yang, Y. (University of Toronto & Vector Institute)
    • Janzing, D. (Amazon)
    • Sachan, M. (ETH Zürich)
    • Schölkopf, B. (Max Planck Institute for Intelligent Systems, Tübingen; ELLIS Institute Tübingen)
    • Jin, Z. (University of Toronto & Vector Institute; ETH Zürich; Max Planck Institute for Intelligent Systems, Tübingen)
    • Shrestha, R. B.* (University of Toronto & Vector Institute)

  • Causal Effect Estimation with Latent Textual Treatments

    Venugopal, A. (Stanford University); Spiess, J. (Stanford University); Feder, A. (Hebrew University of Jerusalem); Feldman, O. (Hebrew University of Jerusalem)

  • Learning Whom to Discharge: Causal Machine Learning for ICU Patient Prioritization Under Capacity Constraints

    Vogel, J. (University of St. Gallen); Filipovic, M. (Cantonal Hospital of St. Gallen); Cordier, J. (University of St. Gallen)

  • Causal Tutor: An Interactive Learning and Research Tool for Causal Inference

    Akram, Z. M., Allam, Y. … Shrestha, R. B.

    All 10 Authors & Affiliations
    • Akram, Z. M. (University of Toronto & Vector Institute)
    • Allam, Y. (University of Toronto & Vector Institute)
    • Barsainyan, A. A. (University of Toronto & Vector Institute)
    • El Herraoui, O. (TU Darmstadt)
    • Uemura, K. (University of Toronto & Vector Institute)
    • Sun, X. (The University of Hong Kong)
    • Zhang, T. J. (University of Toronto & Vector Institute; EuroSafeAI)
    • Schölkopf, B. (Max Planck Institute for Intelligent Systems, Tübingen; ELLIS Institute Tübingen)
    • Jin, Z. (University of Toronto & Vector Institute; Max Planck Institute for Intelligent Systems, Tübingen; EuroSafeAI)
    • Shrestha, R. B.* (University of Toronto & Vector Institute; Max Planck Institute for Intelligent Systems, Tübingen)

  • When Does Double Machine Learning Improve Panel Causal Inference? Evidence from the Medicaid Expansion and Medicare Part D

    Adams, C. (US Congressional Budget Office); Montano, A. (US Congressional Budget Office); Schulman, E. (US Congressional Budget Office)

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Break

November 5

Keynote

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Teppo Felin

University of Utah
David Eccles School of Business

Ion Foundation Endowed Professor and Co-Executive Director of the ION Management Science Lab.

His research spans strategy, decision-making, cognition, AI and causal reasoning, including the theory-based view of strategy.

Felin was Professor of Strategy at Oxford’s Saïd Business School from 2013–2021 and directed the Oxford Diploma in Strategy & Innovation from 2016–2021. His work has appeared in Organization Science, Academy of Management Review, MIT Sloan Management Review and Genome Biology. A native of Helsinki, he has also held appointments at Emory University, Brigham Young University and Lund University.

Website Add the Keynote to Your Calendar
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Break

November 5

Session 05

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  • Estimating Substantive Significance

    Kirkland, J. H. (University of Virginia); Harden, J. J. (University of Notre Dame)

  • Your Agent Ran the Experiment Wrong: Causal Guardrails for Analytical Agents

    Shankar, A. (Atlassian)

  • Omitted Variable Bias in Difference-in-Differences Designs

    Sant’Anna, P. H. C. (Emory University); Chernozhukov, V. (MIT); Cinelli, C. (University of Washington); Wang, J. (University of Washington)

  • Estimating Heterogeneous Treatment Effects at Scale: A Marginal Treatment Effects Pipeline for Product Experimentation

    Arora, M. (Google)

Presenting author, where confirmed.

Looking for an earlier program?

Browse the 2020–2025 Archive