All seminars are on Tuesdays at 8:30 am PST / 11:30 am EST / 4:30 pm London time / 00:30 am(+1day) Beijing time. Please note: Due to recent daylight-saving time changes, the meeting time in your local time zone may have shifted.

You can join the webinar on Zoom here (webinar ID: 968 8371 7451). The password is 414559. Past talks are available here.

Upcoming Seminars

• 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)
  [Paper]

• Tuesday, Nov 11, 2025 [SCI+OCIS]:
  - Panel discussion: Funding Your Work: Thinking Beyond the NIH
  - Panelists: Michael Thompson (University of Michigan), Shu Yang (North Carolina State University)

• Tuesday, Nov 18, 2025: Caleb Miles (Columbia University)
  - Title: Addressing an extreme positivity violation to distinguish the causal effects of surgery and anesthesia via separable effects
  - Abstract: The U.S. Food and Drug Administration has cautioned that prenatal exposure to anesthetic drugs during the third trimester may have neurotoxic effects; however, there is limited clinical evidence available to substantiate this recommendation. One major scientific question of interest is whether such neurotoxic effects might be due to surgery, anesthesia, or both. Isolating the effects of these two exposures is challenging because they are observationally equivalent, thereby inducing an extreme positivity violation. To address this, we adopt the separable effects framework of Robins and Richardson (2010) to identify the effect of anesthesia (alone) by blocking effects through variables that are assumed to completely mediate the causal pathway from surgery to the outcome. We apply this approach to data from the nationwide Medicaid Analytic eXtract (MAX) from 1999 through 2013, which linked 16,778,281 deliveries to mothers enrolled in Medicaid during pregnancy. Furthermore, we assess the sensitivity of our results to violations of our key identification assumptions.
  - Discussant: James Robins (Harvard University)
  [Paper]

• Tuesday, Nov 25, 2025: Thanksgiving break (no seminar)

• Tuesday, Dec 02, 2025: Rajen Shah (University of Cambridge)
  - Title: Hunt and test for assessing the fit of semiparametric regression models
  - Abstract: We consider testing the goodness of fit of semiparametric regression models, such as generalised additive models, partially linear models, and quantile additive regression models: a class of problems that includes, for example, testing for heterogeneous treatment effects. We propose an approach that involves splitting the data in two parts. On one part, we "hunt" for any signal that may be present in the score-type residuals following a fit of the null model. On the remaining data, we test for the presence of the potential signal thus found.  In the first, hunting stage of the procedure, our framework allows the use of any flexible regression method chosen by the practitioner, such as a random forest. The method can therefore harness the power of modern machine learning tools to detect complex alternatives. A challenge in the testing step is that any first-order bias in the residuals may lead to rejection under the null. To address this, we employ a debiasing step, which we show is equivalent to performing a particular weighted least squares regression. We establish that the type I error can be controlled under relatively mild conditions and that the test has power against alternatives for which, with high probability, the hunted signal is correlated with the true signal in the score residuals.
  - Discussant: Mats Stensrud (EPFL)

• Tuesday, Dec 09, 2025: Carlos Cinelli (University of Washington)
  - Title: TBA
  - Abstract: TBA
  - Discussant: TBA

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

Mailing List

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Suggest a speaker

If there is anyone you would like to hear at the Online Causal Inference Seminar, you may let us know here.

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