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, October 19, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker #1: Juan Correa (Columbia University & Universidad Autónoma de Manizales)
    - Title:
    Generalizing the Effect of Soft Interventions
    Abstract: The challenge of generalizing causal knowledge across different environments is pervasive in scientific explorations, including in AI, ML, and Data Science. Experiments are usually performed in one environment/domain (e.g., in a lab, on Earth) with the intent, almost invariably, of being used elsewhere (e.g., outside the lab, on Mars), where the conditions are likely to be different. In the causal inference literature, this generalization task has been formalized under the rubric of transportability, for which several criteria and algorithms have been developed in the context of atomic, do-interventions. However, many real-world applications require more complex, stochastic interventions, often called "soft" interventions.
    In this work, we extend transportability theory to generalize the effect of soft interventions, which could appear both in the input and target distributions. Specifically, we develop a graphical condition that is both necessary and sufficient for deciding soft transportability. Second, we develop an algorithm to determine whether the effect of a soft intervention is computable from a combination of the distributions available across domains.

    - 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: Carlos Cinelli (University of Washington)
    - Title: TBD

  • Tuesday, November 2, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Xinran Li (UIUC)
    - Title:
    Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
    - Discussant: Panos Toulis (Chicago Booth)
    - Abstract: Randomization (a.k.a. permutation) inference is typically interpreted as testing Fisher's "sharp" null hypothesis that all effects are exactly zero. This hypothesis is often criticized as uninteresting and implausible. We show, however, that many randomization tests are also valid for a "bounded" null hypothesis under which effects are all negative (or positive) for all units but otherwise heterogeneous. The bounded null is closely related to important concepts such as monotonicity and Pareto efficiency. Inverting tests of this hypothesis yields confidence intervals for the maximum (or minimum) individual treatment effect. We then extend randomization tests to infer other quantiles of individual effects, which can be used to infer the proportion of units with effects larger (or smaller) than any threshold. The proposed confidence intervals for all quantiles of individual effects are simultaneously valid, in the sense that no correction due to multiple analyses is needed. In sum, we provide a broader justification for Fisher randomization tests, and develop exact nonparametric inference for quantiles of heterogeneous individual effects. We illustrate our methods with simulations and applications, where we find that Stephenson rank statistics often provide the most informative results.

  • Tuesday, November 9, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Jin Tian (Iowa State University)
    - Title: TBD

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

  • Tuesday, November 30, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Thomas Richardson (University of Washington)
    - Title: TBD

  • Tuesday, December 7, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Jann Spiess (Stanford University)
    - Title: TBD

  • Tuesday, December 14, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Ruoxuan Xiong (Emory University)
    - Title:
    Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference
    - Abstract: In many observational studies in social science and medical applications subjects or individuals are connected and one unit's treatment and attributes may affect another unit's treatment and outcome violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible inference many previous works assume the ``exchangeability'' of interfering units under which the effect of interference is captured by the number or ratio of treated neighbors. However in many applications with distinctive units interference is heterogeneous. In this paper we focus on the partial interference setting and restrict units to be exchangeable conditional on observable characteristics. Under this framework we propose generalized augmented inverse propensity weighted (AIPW) estimators for general causal estimands that include direct treatment effects and spillover effects. We show that they are consistent asymptotically normal semiparametric efficient and robust to heterogeneous interference as well as model misspecifications. We also apply our method to the Add Health dataset and find that smoking behavior exhibits interference on academic outcomes.

  • Tuesday, January 18, 2021 [Link to join] (ID: 996 2837 2037, Password: 386638)
    - Speaker: Sach Mukherjee (University of Cambridge)
    - 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.