Online Causal Inference Seminar
Online Causal Inference Seminar
A regular international causal inference seminar. Sign up to our mailing list to receive announcements.
All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 pm Beijing time.
Zoom link and other details are provided below. Past talks are available here. Recordings of past webinars are available on our YouTube channel (subscribe to get notified!).
Tuesday, June 16, 2026: OCIS+INI joint webinar
- Speaker: Aurélien Bibaut (Netflix)
- Time: 7:45 am PT / 10:45 am ET / 3:45 pm London time (Earlier than the usual)
- Zoom details: Link to join, Meeting ID: 814 3038 2346, Passcode: Newton1 (Alt link: YouTube)
- Title: Instrumental Variable Analysis Without Structural Equations
- Abstract: We consider debiased inference on least-squares solutions to inverse problems as a way to avoid having to assume exact solutions exist. Such assumptions are substantive and not innocuous and their failure may well imperil inference when we impose them on the statistical model. Our approach instead allows us to conduct inference on a quantity that is defined regardless of solutions existing and coincides with the usual estimands when they do. For the case of instrumental variables, this means we can motivate the analysis with structural models but these do not need to hold exactly for the inferential procedure to remain valid.
Tuesday, June 23, 2026:
- Speaker: Falco Bargagli Stoffi (University of California, Los Angeles)
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: Causal Stability Selection
- Abstract: Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the expected number of false discoveries, risking spurious findings that fail to replicate. We introduce causal stability selection, an algorithm that combines cross-fitted estimation of conditional average treatment effects with integrated path stability selection. The method accommodates arbitrary treatment effect estimators and arbitrary base selectors, and produces a selection set with an explicit, non-asymptotic bound on the expected number of false positives. Under standard causal identifying assumptions and regularity conditions on the base selector, we prove that the estimated selection probabilities converge to their oracle counterparts at the rate of the underlying treatment effect estimator. This establishes a direct connection between treatment effect estimation and effect modifier discovery. We illustrate the method on a randomized trial in oncology and on observational data on maternal smoking and infant birthweight.
- Discussant: Melody Huang (Yale University)
[Paper]
Recordings of our past webinars are available on YouTube. Follow us on YouTube to stay notified!
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If there is anyone you would like to hear at the Online Causal Inference Seminar, you may let us know here.
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.
Recordings of our past webinars are available on YouTube. Follow us on YouTube to stay notified!
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.
Oliver Dukes (Ghent), Naoki Egami (Columbia), Aditya Ghosh (Stanford), Guido Imbens (Stanford), Ying Jin (Wharton), Sara Magliacane (U of Amsterdam), Razieh Nabi (Emory), Ema Perkovic (U of Washington), Dominik Rothenhäusler (Stanford), Rahul Singh (Harvard), Mats Stensrud (EPFL), Qingyuan Zhao (Cambridge)
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)
If you have feedback or suggestions, please e-mail us at onlinecausalinferenceseminar@gmail.com.
We gratefully acknowledge support by the Stanford Department of Statistics and the Stanford Data Science Initiative.
You can join the webinar by clicking the link on the webpage. If you signed up to the mailing list, you will receive an email with the link before the webinar begins. On Tuesday, you should join the seminar shortly before the start time 8:30 am PT.
Due to high demand, we will host the seminar as a Zoom webinar. As an attendee, you will not be able to unmute yourself. If you have questions about the content of the talk, please submit the questions using the Zoom Q&A feature. Time permitting, and depending on the volume of questions, the moderator will either ask your question for you or confirm with you to ask the question yourself and unmute you at a suitable time. In some meetings, the collaborators of the speaker will be online to address your questions in Q&A. Note that Q&A will be moderated by us so you will only be able to see some of the questions of the other attendees. If you want to send messages to the moderators during the seminar, please use the Zoom chat feature.
If you have not used Zoom before, we highly recommend downloading and installing the Zoom client before the meeting. Additional instructions on how to use Zoom during a webinar can be found here. Note that for the online causal inference seminar, we do not require registration in advance so you will be able to join by simply clicking the link on this webpage or in the email.
If you have further questions, please drop us an email at onlinecausalinferenceseminar@gmail.com