Online Causal Inference Seminar

A regular international causal inference seminar.

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Upcoming Seminar Presentations

All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 5:30 pm Berlin / 11:30 pm Beijing, except on March 16 and 23, when due to daylight-savings time, they will be at 8:30 am PT / 11:30 am ET / 3:30 pm London / 4:30 pm Berlin / 11:30 pm Beijing.

  • Tuesday, September 21, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Ted Westling (University of Massachusetts, Amherst)
    - Title:
    Nonparametric tests of the causal null with non-discrete exposures
    Discussant: Oliver Dukes (University of Pennsylvania)
    - Abstract: Many methods have been developed to test for the presence of a causal effect of a discrete exposure on an outcome when there are no unobserved confounders. In this talk, we introduce a class of nonparametric tests of the null hypothesis that there is no average causal effect of an arbitrary univariate exposure on an outcome when there are no unobserved confounders. Our tests apply to discrete, continuous, and mixed discrete-continuous exposures. We demonstrate that our proposed tests are doubly-robust consistent, that they have correct asymptotic type I error if both nuisance parameters involved in the problem are estimated at fast enough rates, and that they have power to detect local alternatives approaching the null at the rate $n^{-1/2}$. We study the performance of our tests in numerical studies, and use them to test for the presence of a causal effect of BMI on immune response in early-phase vaccine trials.

  • Tuesday, September 28, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Youjin Lee (Brown University)
    - Title:
    Evidence factors from multiple, possibly invalid, instrumental variables
    Discussant: Jose Zubizarreta (Harvard University)
    - Abstract: Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome in the presence of unmeasured confounders. When several instrumental variables are available and the instruments are subject to possible biases that do not completely overlap, a careful analysis based on these several instruments can produce orthogonal pieces of evidence (i.e., evidence factors) that would strengthen causal conclusions when combined. We develop several strategies, including stratification, to construct evidence factors from multiple candidate instrumental variables when invalid instruments may be present. Our proposed methods deliver nearly independent inferential results each from candidate instruments under the more liberally defined exclusion restriction than the previously proposed reinforced design. We apply our stratification method to evaluate the causal effect of malaria on stunting among children in Western Kenya using three nested instruments that are converted from a single ordinal variable. Our proposed stratification method is particularly useful when we have an ordinal instrument of which validity depends on different values of the instrument.
    This is based on joint work with Anqi Zhao, Dylan Small, and Bikram Karmarkar.

  • Tuesday, October 5, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Eleanor Sanderson (University of Bristol)
    - Title: TBD

  • Tuesday, October 12, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Colin Fogarty (MIT)
    - Title: TBD

  • Tuesday, October 19, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker #1: Juan Correa (Columbia University & Universidad Autónoma de Manizales)
    - Title: TBD

    - Speaker #
    2: Nicola Gnecco (University of Geneva)
    - Title:
    Causal discovery in heavy-tailed models
    - Abstract:
    Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms only manifest themselves in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population case, the causal tail coefficient is shown to reveal the causal structure if the distribution follows a linear structural causal model. This holds even in the presence of latent common causes that have the same tail index as the observed variables. Based on a consistent estimator of the causal tail coefficient, we propose a computationally highly efficient algorithm that estimates the causal structure. We prove that our method consistently recovers the causal order and we compare it to other well-established and nonextremal approaches in causal discovery on synthetic and real data. The code is available as an open-access R package.

  • Tuesday, October 26, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Shohei Shimizu (Shiga University and RIKEN)
    - Title:
    A semiparametric approach to causal discovery
    - Abstract: Causal discovery is a methodology for inferring causal graphs of variables from data. A classical approach for causal discovery tries to infer the underlying causal graph without making any specific assumptions about the functional form or distribution. However, this nonparametric approach can only find a class of equivalent causal graphs in many cases. Therefore, a more recent approach, which we here call the semiparametric approach, makes some assumptions on the distributions and/or functional forms, e.g., non-Gaussian continuous distributions and linearity. Under such assumptions, we can uniquely identify the causal graphs or have smaller numbers of equivalent causal graphs than the nonparametric approach.

    In this talk, I first briefly review the semiparametric approach for causal discovery and its applications ( Then, I introduce our recent works on the semiparametric causal discovery. These works base the idea of Linear Non-Gaussian Acyclic Models, LiNGAM. We first consider a method to estimate causal structure graphs in the presence of hidden common causes. The method first finds confounded observed variables between which unobserved common causes are likely to exist. Then it estimates the causal relations of the other unconfounded observed variables. We also consider a method to infer the causal graph of latent factors from multiple datasets obtained under different conditions. It further analyzes which latent factors are common to many conditions and which are specific to some conditions.

  • Tuesday, November 16, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Linbo Wang (University of Toronto)
    - Title: TBD

  • Tuesday, January 25, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Beth Ann Griffin (RAND Corporation)
    - Title: TBD

Format and Rules

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 chat.

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.


Michael Celentano (Stanford), Guido Imbens (Stanford), Georgia Papadogeorgou (University of Florida), Ema Perkovic (University of Washington), Dominik Rothenhäusler (Stanford), Qingyuan Zhao (University of Cambridge)

Organizing committee

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)

Feedback and Suggestions

If you have feedback or suggestions or want to propose a speaker, please e-mail us at


We gratefully acknowledge support by the Stanford Department of Statistics and the Stanford Data Science Initiative.