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!).

Upcoming Seminars

• Tuesday, May 26, 2026:  OCIS+INI joint webinar
  - Speaker: James Robins (Harvard University)
  - Time: (30 mins earlier than usual) This event starts at 8 am PT/ 11 am ET/ 4 pm London time/ 11 pm Beijing time
  - Details: Live-streamed on  https://www.newton.ac.uk/news/watch-live/ (Alt. Link: https://www.youtube.com/@iniseminarroom1/live)
  - Title:  Rothschild Public Lecture | Forty years of causal inference: Report of a great-grandfather
  - Abstract: Forty years ago, the following disciplines had their own languages, opinions and idiosyncrasies re causal inference: philosophy, computer science, sociology, psychology, statistics, epidemiology, political science, and economics. Today all speak a common language. Top journals have gone from knee-jerk rejection to active solicitation of articles on causal inference. The ongoing rapid development of the field has been driven by:
  1. End of the historical suppression of causal language in statistics and medicine (aside from randomized clinical trials);
  2. The internet making cross disciplinary understanding and collaboration easy;
  3. The need for individualized treatment regimes in Medicine;
  4. Tech companies realizing that optimizing profits depended on causal interventions rather than just prediction;
  5. The development of causal graphs that offers non-technical users the ability to validly reason about complex causal systems;
  6. The existence of huge data sets leading to data driven science rather than hypothesis driven science.
  In my lecture, I will give a history of statistical methods for causal inference, focusing on methods developed by myself and colleagues. I will explain why causal methods have had such a large impact in substantive areas in which confounding by time varying covariates is very strong, as in studies of HIV-infected individuals. These causal methods are also an integral part of the target trial methodology - a methodology that is altering the analytical paradigm for the estimation of causal effects from longitudinal observational data in Medicine and Public Health. I will conclude with a discussion of the future of causal inference in the coming age of AI.

• Tuesday, June 02, 2026:
  - Speaker: Suhas Vijaykumar (U.C. San Diego)
  - Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
  - Title: Demonstration Experiments
  - Abstract: Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatment effect but to demonstrate that at least one candidate intervention yields a positive effect, for some subpopulation and on some measured outcome. We formalize this objective as testing the global null in a threshold bandit framework, and develop two inference procedures that are valid under general adaptive sampling: one that pools information across promising arms, and one based on time-uniform multiple testing of individual arm means. To support the latter, we establish a moderate-deviations principle for the sequential t-statistic, justifying asymptotic confidence sequences in settings where the number of arms is large relative to the sample size. To illustrate how adaptive designs can target the proposed statistics, we recast experimental design as bandit optimization with an arm's reward given by its signal-to-noise ratio, and analyze an allocation rule for which we establish a logarithmic regret bound. We apply the methods in a simulation study of targeting unconditional cash transfer programs. Joint work with Guido Imbens, Lorenzo Masoero, Alexander Rakhlin and Thomas Richardson.
  - Discussant: Aurélien Bibaut (Netflix)
  [Paper]

• Tuesday, June 09, 2026: OCIS+INI joint webinar
  - Speaker: Yixin Wang (University of Michigan)
  - Details: TBA

• Tuesday, June 16, 2026:  OCIS+INI joint webinar (Details TBA)

• 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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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.

Youtube channel

Recordings of our past webinars are available on YouTube. Follow us on YouTube to stay notified!

Instructions for Attendees

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.

Please participate during the seminar!

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. 

Zoom instructions

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