Online Causal Inference Seminar
Online Causal Inference Seminar
A regular international causal inference seminar.
All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 11:30 pm Beijing.
You can join the webinar on Zoom here (webinar ID: 968 8371 7451). The password is 414559. Past talks are available here.
Tuesday, Sep 23, 2025: Young researchers' seminar
Speaker 1: Jiaqi Zhang (MIT)
- Title: Learning causal cellular programs from large-scale perturbations
- Abstract: Complex molecular mechanisms govern cellular functions in living organisms and shape their behavior in health and disease. Understanding these mechanisms can greatly accelerate therapeutic discovery, yet it remains challenging due to the high dimensionality and intricate dependencies within biological systems. Recent advances in experimental technologies, however, are beginning to make this problem more tractable. For example, we can now systematically perturb individual or combinations of genes in single cells and measure their downstream effects, enabling empirical identification and validation of causal relationships. Nevertheless, perturbation data remain noisy and high-dimensional, with effects often sparse and subtle.
In this talk, I will present our work addressing two key challenges in this emerging paradigm: (1) how to define and learn the causal programs that govern high-dimensional or perceptual data; (2) how to model perturbational effects in a way that enables the prediction of novel perturbations. For (1), we introduce new causal representation theories that guarantee identifiability of the underlying causal programs, given sufficient samples and regularizing condition. For (2), we develop a modular framework that models perturbation effects through distributional discrepancies. This approach captures nuanced, sample-level changes and enables extrapolation to predict the effects of unseen perturbations across diverse conditions and data types.
[Slides]
Speaker 2: Sizhu Lu (UC Berkeley)
- Title: Estimating treatment effects with competing intercurrent events in randomized controlled trials
- Abstract: The analysis of randomized controlled trials is often complicated by intercurrent events (ICEs) -- events that occur after treatment initiation and affect either the interpretation or existence of outcome measurements. Examples include treatment discontinuation or the use of additional medications. In two recent clinical trials for systemic lupus erythematosus with complications of ICEs, we classify the ICEs into two broad categories: treatment-related (e.g., treatment discontinuation due to adverse events or lack of efficacy) and treatment-unrelated (e.g., treatment discontinuation due to external factors such as pandemics or relocation). To define a clinically meaningful estimand, we adopt tailored strategies for each category of ICEs. For treatment-related ICEs, which are often informative about a patient's outcome, we use the composite variable strategy that assigns an outcome value indicative of treatment failure. For treatment-unrelated ICEs, we apply the hypothetical strategy, assuming their timing is conditionally independent of the outcome given treatment and baseline covariates, and hypothesizing a scenario in which such events do not occur. A central yet previously overlooked challenge is the presence of competing ICEs, where the first ICE censors all subsequent ones. Despite its ubiquity in practice, this issue has not been explicitly recognized or addressed in previous data analyses due to the lack of rigorous statistical methodology. In this paper, we propose a principled framework to formulate the estimand, establish its nonparametric identification and semiparametric estimation theory, and introduce weighting, outcome regression, and doubly robust estimators. We apply our methods to analyze the two systemic lupus erythematosus trials, demonstrating the robustness and practical utility of the proposed framework.
[Slides][Paper]
Tuesday, Sep 30, 2025: Joseph Antonelli (University of Florida)
- Title: Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
- Abstract: Estimating the health effects of multiple air pollutants is a crucial problem in public health, but one that is difficult due to unmeasured confounding bias. Motivated by this issue, we develop a framework for partial identification of causal effects in the presence of unmeasured confounding in settings with multiple treatments and multiple outcomes. Under a factor confounding assumption, we show that joint partial identification regions for multiple estimands can be more informative than considering partial identification for individual estimands one at a time. We show how assumptions related to the strength of confounding or magnitude of plausible effect sizes for one estimand can reduce the partial identification regions for other estimands. As a special case of this result, we explore how negative control assumptions reduce partial identification regions and discuss conditions under which point identification can be obtained. We develop novel computational approaches to finding partial identification regions under a variety of these assumptions. We then estimate the causal effect of PM2.5 components on a variety of public health outcomes in the United States Medicare cohort, where we find that, in particular, the detrimental effect of black carbon is robust to the potential presence of unmeasured confounding bias.
- Discussant: TBA
[Slides][Paper][Discussant slides]
Tuesday, Oct 07, 2025 [SCI+OCIS]: Miguel Hernán (Harvard University)
Tuesday, Oct 14, 2025: Jakob Runge (University of Potsdam)
Tuesday, Oct 21, 2025: Wooseok Ha (Korea Advanced Institute of Science and Technology)
Tuesday, Oct 28, 2025: Linbo Wang (University of Toronto)
Tuesday, Nov 04, 2025: Zijun Gao (USC Marshall Business School)
Tuesday, Nov 11, 2025 [SCI+OCIS]: TBA
Tuesday, Nov 18, 2025: TBA
Tuesday, Nov 25, 2025: Thanksgiving break
Tuesday, Dec 02, 2025: Rajen Shah
Tuesday, Dec 09, 2025: Carlos Cinelli
Recordings of our past webinars are available on YouTube. Follow us on YouTube to stay notified!
briel Loewinger, PhD (NIH), Emre Kiciman, PhD (Microsoft), Natalie Levy, PhD (Aetio
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.
Naoki Egami (Columbia), Aditya Ghosh (Stanford), Guido Imbens (Stanford), Ying Jin (Harvard), Sara Magliacane (U of Amsterdam), Razieh Nabi (Emory), Georgia Papadogeorgou (U of Florida), Ema Perkovic (UWashington), Dominik Rothenhäusler (Stanford), 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