CDSM 2026 / PROGRAMME & SPEAKERS

Programme &
speakers 2026.

4–5 November 2026Online

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

Back to the meeting

DAY 01 / WEDNESDAY

4 November 2026

Session 01

–

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

    Bugaev, A. (Bolt)

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

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

  3. Causal Graphs for Conditional Parallel Trends

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

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

    Patel, V. (Insytes GmbH)

11:50–13:00

Break

Session 02

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  1. Counterfactual Optimization of Policy Interventions: Lexical Ordering and Leapfrogging

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

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

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

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

    Vincent, B. (PyMC Labs)

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

    Jervis, J. (Boston Scientific)

14:20–14:40

Break

Session 03

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  1. Graph Preconditioning for High-Dimensional Fixed Effects Regression

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

  2. Causal Inference and Model Explainability Tools for Retail

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

  3. 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)

  4. Wasserstein Causal Forests for Distribution-Valued Outcomes

    Gobato Souto, H. (University of São Paulo)

16:00–16:20

Break

Debate

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IN CONVERSATION

In debate.

17:40–18:00

Break

Session 05

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

    Arora, M. (Google)

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

    Jaeyong Jung (Colorado State)

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

    Opdyke, J. D. (DataMineit, LLC)

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

    Shankar, A. (Atlassian)

DAY 02 / THURSDAY

5 November 2026

Session 01

–

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

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

  2. Foundation Models for Causal Effect Estimation and Conditional Discovery in Marketing

    Trujillo, C. (PyMC Labs)

  3. Dirichlet-Tree Allocation Priors for Bayesian Causal Forests

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

  4. 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)

11:50–13:00

Break

Session 02

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  1. Better Business Decisions through a New Causal Perspective

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

  2. Automatic Debiased Machine Learning and Sensitivity Analysis for Sample Selection Models

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

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

    Forde, N. (Pandadoc)

  4. The Bright Side of Circuit Breakers

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

14:20–14:40

Break

Spotlight session

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  1. 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)

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

    Zeinaliashtiyani, P. (University of Naples Federico II)

  3. 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”)

  4. 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)

  5. 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)

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

    Tsoumas, I. (National Observatory of Athens)

  7. 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)

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

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

    All 13 authors & affiliations
    • Acharya, S. (University of Toronto & Vector Institute; Stanford University)
    • Shrestha, R. B. (University of Toronto & Vector Institute)
    • 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 Tübingen)
    • 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)
16:00–16:20

Break

Keynote

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2026 KEYNOTE

Teppo Felin

University of Utah
David Eccles School of Business

Ion Presidential Endowed Chair and Co-Director of the Ion Management Science Lab.

Strategy, entrepreneurship and innovation, including the role of theory and experimentation in learning from data.

Website ↗
Read the full biography

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.

17:40–18:00

Break

Session 05

–

  1. The Explanatory Power of Causal Effects

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

  2. Estimating Substantive Significance

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

  3. 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)

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

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

THE 2026 MEETING

Research and
practice.

Invited talks, presentations of accepted proposals, and discussions with researchers and practitioners from academia and industry.

Join online on 4–5 November 2026.

Call for papers closed

This meeting is a space to discuss research and exchange ideas. No conference proceedings will be published.

For questions about a submitted presentation:

submission@causalscience.org

Looking for an earlier programme?

Browse the 2020–2025 archive