Online Causal Inference Seminar

A regular international causal inference seminar.

Mailing List

To stay up-to-date about upcoming presentations and receive Zoom invitations please join our mailing list. You will receive an email to confirm your subscription. If you are already subscribed to our mailing list and would like to unsubscribe, you may unsubcribe here.

Suggest a speaker

If there is anyone you would like to hear at the Online Causal Inference Seminar, you may let us know here.

Opportunities in Causal Inference

Please check out our opportunities in causal inference page for conferences, workshops, and job listings! If you would like us to list an opportunity, please email us at onlinecausalinferenceseminar@gmail.com.

Google Calendar

You can find a calendar of our events here.

Youtube channel

Follow us on youtube!

Upcoming Seminar Presentations

All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 5:30 pm Berlin / 12:30 pm Beijing.


  • Tuesday, December 6, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Jose Zubizarreta (Harvard University)
    - Title: Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference
    - Discussant:
    Mike Baiocchi (Stanford University)
    - Abstract: A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment are used in pursuit of this principle. Basic methods are matching, regression, and weighting. In this talk, we will examine the connections between these methods through their underlying mathematical programs. We will study their strengths and weaknesses in terms of study design, computational tractability, and statistical efficiency. Overall, we will discuss the role of mathematical optimization for the design and analysis of studies of causal effects.

Join us in our special Causality & Fairness seminar series in January, 2023!

  • Tuesday, January 10, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Ilya Shpitser (Johns Hopkins University)
    - Title: TBD


  • Tuesday, January 17, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Moritz Hardt (Max Planck Institute for Intelligent Systems)
    - Title: TBD



  • Tuesday, February 7, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Maya Petersen (UC Berkeley)
    - Title: TBD



  • Tuesday, March 21, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Jessica Young (Harvard University)
    - Title: TBD


Format and Rules

The seminars are held on Zoom and last 60 minutes. Our seminars will typically follow one of three formats:

Format 1: single presentation

  • 45 minutes of presentation

  • 10 minutes of discussion, led by an invited discussant

  • Q&A, time permitting

Format 2: two presentations

  • Two presentations, 25-30 minutes each

  • Q&A, time permitting

Format 3: interview

  • 40-45 minute conversation with leader in causal inference

  • 15-20 minutes of Q&A

A moderator collects audience questions in Q&A section.

Moderators may ask you to unmute yourself to participate in the discussion. Please note that you may be recorded if you activate your audio or video during the seminar.

Moderators

Michael Celentano (Stanford), Guido Imbens (Stanford), Ying Jin (Stanford), Georgia Papadogeorgou (University of Florida), Ema Perkovic (University of Washington), Dominik Rothenhäusler (Stanford), Qingyuan Zhao (University of Cambridge)

Organizing committee

Susan Athey (Stanford), Guillaume Basse (Stanford), Peter Bühlmann (ETH Zürich), Peng Ding (Berkeley), Andrew Gelman (Columbia), Guido Imbens (Stanford), Fabrizia Mealli (Florence), Nicolai Meinshausen (ETH Zürich), Maya Petersen (Berkeley), Thomas Richardson (UW), Dominik Rothenhäusler (Stanford), Jas Sekhon (Berkeley/Yale), Stefan Wager (Stanford)

Feedback and Suggestions

If you have feedback or suggestions, please e-mail us at onlinecausalinferenceseminar@gmail.com.


Acknowledgements

We gratefully acknowledge support by the Stanford Department of Statistics and the Stanford Data Science Initiative.