All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 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.

Zoom link and other details are provided below. Past talks are available here. Recordings of past webinars are available on our YouTube channel.

Upcoming Seminars

• Tuesday, Jan 27, 2025: OCIS+INI joint webinar
  - Speaker: Victor Chernozhukov (MIT)
  - IMPORTANT: This will start at 8 am PT/ 11 am ET/ 4 pm London time/ midnight Beijing time(+1day)
  - Zoom details: Zoom Link, webinar ID: 862 3272 9587, password: Newton1
  - Title: Adventures in Demand Analysis Using AI
  - Abstract: This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on Amazon.com, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly.

• Tuesday, Feb 3rd, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  Young Researchers' Seminar
  - Speaker 1: Herb Susmann (New York University)
  - Title: Non-overlap Average Treatment Effect Bounds
  - Abstract: The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have non-zero probability of either treatment status, is necessary to identify and estimate the ATE. When overlap fails, the standard solution is to change the estimand, and target a trimmed effect in a subpopulation satisfying overlap; however, this no longer addresses the original goal of estimating the ATE. When the outcome is bounded, we demonstrate that this compromise is unnecessary. We derive non-overlap bounds: partial identification bounds on the ATE that do not require overlap. They are the sum of a trimmed effect within the overlap subpopulation and worst-case bounds on the ATE in the non-overlap subpopulation. Non-overlap bounds have width proportional to the size of the non-overlap subpopulation, making them informative when overlap violations are limited -- a common scenario in practice. Since the bounds are non-smooth functionals, we derive smooth approximations of them that contain the ATE but can be estimated using debiased estimators leveraging semiparametric efficiency theory. Specifically, we propose a Targeted Minimum Loss-Based estimator that is root-n consistent and asymptotically normal under nonparametric assumptions on the propensity score and outcome regression. We then show how to obtain a uniformly valid confidence set across all trimming and smoothing parameters with the multiplier bootstrap. This allows researchers to consider many parameters, choose the tightest confidence interval, and still attain valid coverage. We demonstrate via simulations that non-overlap bound estimators can detect non-zero ATEs with higher power than traditional doubly-robust point estimators. We illustrate our method by estimating the ATE of right heart catheterization on mortality.
  [Paper]
  - Speaker 2: Juraj Bodík (University of Lausanne, Switzerland)
  - Title: Causality and Extreme Events: Why Additive Models Can Be Dangerous and What to Do Instead
  - Abstract: Extreme events, outliers, and heavy-tailed behavior are the rule rather than the exception in many causal systems. Although additive-noise models of the form Y=f(X)+e massively dominate the literature, they can be a poor choice in such systems, when causal effects act primarily through the tail or the variance. This can potentially lead to seriously misleading conclusions. In this talk, I discuss two complementary approaches for causal reasoning in such regimes. For i.i.d. data, I introduce Conditionally Parametric Causal Models (CPCM), which move beyond mean-oriented additive formulations to accommodate heteroscedasticity and heavy tails. For time series, where temporal structure can be an ally rather than a nuisance, I present a framework for Granger-type causality in extremes, designed to detect and characterize causal links solely from extreme events.

• Tuesday, Feb 10, 2026:  OCIS+INI joint webinar
  - Speaker: Lin Liu (Shanghai Jiao Tong University (SJTU))
  - IMPORTANT: This will start at 8 am PT/ 11 am ET/ 4 pm London time/ midnight Beijing time(+1day)
  - Zoom details: TBD
  - Title: TBA
  - Abstract: TBA

• Tuesday, Mar 24, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  - Speaker: Falco Bargagli Stoffi (University of California, Los Angeles)
  - Title: TBA
  - Abstract: TBA
  - Discussant: TBA

• Tuesday, Apr 07, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  - Speaker: Thomas Icard (Stanford University)
  - Title: TBA
  - Abstract: TBA
  - Discussant: TBA

• Tuesday, Apr 14, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  - Speaker: Matteo Bonvini (Rutgers University)
  - Title: TBA
  - Abstract: TBA
  - Discussant: TBA

• Tuesday, May 05, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  - Speaker: Martin Tingley (Microsoft)
  - Title: TBA
  - Abstract: TBA
  - Discussant: TBA

• Tuesday, May 19, 2026: Zoom link (webinar ID: 968 8371 7451, password: 414559)  
  - Speaker: Naoki Egami (Massachusetts Institute of Technology)
  - 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

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.

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