Upcoming Seminars

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.

Fall 2025

• 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: Carlos Cinelli (University of Washington)
  [Slides][Paper][Discussant slides]

• Tuesday, Oct 07, 2025  [SCI+OCIS]: Miguel Hernán  (Harvard University)
  - Title: Making decisions is hard but making decisions without data is much harder: How causal inference research helped governments during the last pandemic
  - Abstract: The first question a decision maker asks is “Do we have a problem?”; the second one is “How do we handle the problem?”. Answering the first question requires descriptive studies; answering the second one requires causal studies. This talk describes examples of how this process worked in the real world during the last pandemic. It is partly based on my experience as an embedded researcher in a government agency. Some take-home messages are: conducting good descriptive studies is difficult but indispensable; actionable causal inference can sometimes rely on randomized trials but will often have to rely on observational emulations of trials; sometimes the causal questions are so complex that only mathematical models will help decision makers; and researchers are usually not qualified to tell decision makers which decisions they should make.

• 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: TBA
  [Paper]

• 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 (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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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