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 through Oct 31; 4:30 pm UTC from Nov 1 through year-end).
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, Sep 29, 2026:
- Speaker: Ilya Shpitser (Johns Hopkins University)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: Proximal Identification and Estimation in Front-Door Causal Structures with Unobserved Confounding of the Mediator
- Abstract: Unobserved confounding is a fundamental obstacle in causal inference problems. In the graphical modeling literature, a general theory has been developed that allows identification in the presence of hidden variables, with some limitations. In particular, Pearl's celebrated front-door criterion allows nonparametric identification in the presence of unobserved common causes of the treatment and the outcome, however it requires the presence of an unconfounded variable that mediates all causal influence from the treatment to the outcome. This stringent requirement limits the applicability of the front-door criterion. We propose proximal generalizations of the front-door criterion, allowing both arbitrary treatment/outcome confounding, and unobserved confounders of the mediator, provided informative proxies for the latter type of confounders are observed. In addition to deriving three new identification strategies in this setting, we provide plug-in and influence function-based estimation strategies for the resulting functionals. This is joint work with Helen Guo and Beatrix Wen.
[Paper][Slides][Video]
Tuesday, Oct 06, 2026:
- Speaker: Raaz Dwivedi (Cornell University)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: TBA
- Abstract: TBA
- Discussant: Devavrat Shah (MIT)
[Paper][Slides][Video][Discussion slides]
Tuesday, Oct 13, 2026:
- Speaker: Rohit Bhattacharya (Williams College)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: Testing contagion against confounding: Six degrees of separation as a (scarce) statistical resource
- Abstract: Distinguishing between peer-to-peer influence (contagion) and background similarity (latent confounding or homophily) in observational network studies is famously difficult, especially when given a single realization of the network (Shalizi and Thomas, 2011). In this work, we derive coding likelihood-ratio tests that can be used to separate contagion from confounding in the baseline covariates, treatments, and outcomes of units in full interference settings, but under the assumption that these mechanisms do not co-occur. We also propose estimators for network causal effects that are consistent once the mechanisms of contagion/confounding are known or correctly inferred. These estimators can be viewed as an extension of auto-g-computation (Tchetgen Tchetgen et al., 2021) to handle unmeasured confounding between units. Surprisingly, to achieve standard asymptotics for our proposed methods from dependent network data, it is sufficient to use a growing number of units that are pairwise at least six degrees of separation away from each other. This converts a famous piece of social-network folklore into a formal statistical resource, albeit a scarce one. We evaluate its affordability across multiple online social networks. The talk will include discussions on the limitations of our methods, the statistical and causal interpretations of the graphical models of interference that we use to develop our tests and estimators, and the statistical challenges and impossibility results that arise when relaxing the assumption of mutually exclusive mechanisms.
- Discussant: TBA
[Paper][Slides][Video][Discussion slides]
Tuesday, Oct 20, 2026 [SCI+OCIS]:
- Speaker: Nandita Mitra (University of Pennsylvania)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: TBA
- Title: TBA
- Abstract: TBA
This event is joint with the Society for Causal Inference.
Tuesday, Oct 27, 2026:
- Speaker: Christopher Harshaw (Columbia University)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: A Design-Based Minimax Theory for Network Experiments
- Abstract: Network experiments are used throughout the social and medical sciences to investigate causal effects under the presence of interference. While a large body of work has developed improved statistical procedures, the fundamental limits of statistical estimation in these settings is less well understood. In this talk, I will introduce a design-based theory of minimax risk for network experiments under an arbitrary neighborhood interference model. Our notion of minimax risk describes the optimal precision among all statistical procedures for investigating a particular causal effect on the observed interference network. We show that the minimax risk is a function of the corresponding conflict graph, which captures inherent unobservability of estimand-relevant potential outcomes given the observed interference network. Our main contribution is a series of upper and lower bounds on the minimax rate in terms of local and global connectivity properties of the conflict graph. To illustrate their utility, we apply these general results to obtain minimax analyses for two commonly studied effects: the direct treatment effect and global average treatment effect.
- Discussant: TBA
[Paper][Slides][Video][Discussion slides]
Tuesday, Nov 10, 2026:
- Speaker: Emilija Perković ( University of Washington)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: TBA
- Abstract: TBA
Tuesday, Nov 17, 2026:
- Speaker: Jonathan Roth ( Brown University.)
- Time: 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 11:30 Beijing time
- Details: Zoom link, Meeting ID: 968 8371 7451, Passcode: 414559
- Title: TBA
- Abstract: TBA
Recordings of our past webinars are available on YouTube. Follow us on YouTube to stay notified!
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.
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 University),
Naoki Egami (MIT),
Aditya Ghosh (Stanford University),
Christopher Harshaw (Columbia University),
Ying Jin (University of Pennsylvania),
Sara Magliacane (Saarland University, University of Amsterdam),
Razieh Nabi (Emory University),
Dominik Rothenhäusler (Stanford University),
Rahul Singh (Harvard University),
Mats Stensrud (EPFL),
Ting Ye (University of Washington).
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