A regular international causal inference seminar.
All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 3:30 pm UTC / 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, Oct 14, 2025: Jakob Runge (University of Potsdam)
- Title: Causal Inference on Time Series Data with the Tigramite Package
- Abstract: This talk introduces the open-source Python package Tigramite, which implements constraint-based algorithms such as PCMCI+ and many variants thereof as methods optimised for causal discovery on time series. In addition, Tigramite features causal effect estimation using optimal adjustment. I will outline the basic ideas behind PCMCI and optimal adjustment and then demonstrate practical workflows in Tigramite, including a user-friendly guide to choosing methods in causal inference based on causal questions, assumptions and available data. I look forward to feedback and exchange on improvements of the package to make causal inference accessible for practitioners dealing with time series data.
Tuesday, Oct 21, 2025: Wooseok Ha (Korea Advanced Institute of Science and Technology)
- Title: When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
- Abstract: Semi-supervised domain adaptation (SSDA) aims to achieve high predictive performance in the target domain with limited labeled target data by exploiting abundant source and unlabeled target data. Despite its significance in numerous applications, theory on the effectiveness of SSDA remains largely unexplored, particularly in scenarios involving various types of source-target distributional shifts. In this talk, I will present a theoretical framework based on structural causal models (SCMs) which allows us to analyze and quantify the performance of SSDA methods when labeled target data is limited. Within this framework, I introduce three SSDA methods, each having a fine-tuning strategy tailored to a distinct assumption about the source and target relationship. Under each assumption, I demonstrate how extending an unsupervised domain adaptation (UDA) method to SSDA can achieve minimax-optimal target performance with limited target labels. Finally, when the relationship between source and target data is only vaguely known—a common practical concern—I will describe the Multi Adaptive-Start FineTuning (MASFT) algorithm, which fine-tunes UDA models from multiple starting points and selects the best-performing one based on a small hold-out target validation dataset. Combined with model selection guarantees, MASFT achieves near-optimal target predictive performance across a broad range of types of distributional shifts while significantly reducing the need for labeled target data.
- Discussant: Jason Kluswoski (Princeton University)
[Paper]
Tuesday, Oct 28, 2025: Linbo Wang (University of Toronto)
- Title: The synthetic instrument: From sparse association to sparse causation
- Abstract: In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data such as the lasso assume the associations between the exposures and the outcome are sparse. These methods, however, do not estimate the causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects in view are sparse. We show that with sparse causation, the causal effects are identifiable even with unmeasured confounding. At the core of our proposal is a novel device, called the synthetic instrument, that in contrast to standard instrumental variables, can be constructed using the observed exposures directly. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an $\ell_0$-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.
- Discussant: TBA
[Paper]
Tuesday, Nov 04, 2025: Zijun Gao (USC Marshall Business School)
- Title: Explainability and Analysis of Variance
- Abstract: Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. We propose a new notion called counterfactual explainability for causal attribution that is motivated by the concept of genetic heritability in twin studies. Counterfactual explainability extends methods for global sensitivity analysis (including the functional analysis of variance and Sobol's indices), which assumes independent explanatory variables, to dependent explanations by using a directed acyclic graphs to describe their causal relationship. Therefore, this explanability measure directly incorporates causal mechanisms by construction. Under a comonotonicity assumption, we discuss methods for estimating counterfactual explainability and apply them to a real dataset dataset to explain income inequality by gender, race, and educational attainment.
- Discussant: Art Owen (Stanford University)
Tuesday, Nov 11, 2025 [SCI+OCIS]: TBA
Tuesday, Nov 18, 2025: TBA
Tuesday, Nov 25, 2025: Thanksgiving break
Tuesday, Dec 02, 2025: Rajen Shah (University of Cambridge)
Tuesday, Dec 09, 2025: Carlos Cinelli (University of Washington)
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
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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