Spring School on Causality

Sorbonne Center for Artificial Intelligence (SCAI), Paris, France

From March 28th to March 31st, 2023

Location: Sorbonne University Pierre and Marie Curie Campus, 4 Place Jussieu, 75005 Paris, France
Image credit to iEES Paris


The Spring School on Causality will be held at the Sorbonne Center for Artificial Intelligence (SCAI), Paris, France on March 28-31, 2023.

Participation is free of charge, but registration is mandatory.

Please register here before March 24th AoE. Please let us know if you need letters for visa application.

If you need an attendance attestation, please complete the form at forms.gle/LjCYMGqZvMjo8jdu7.


Click on the title of the lectures to download their slides. They are also available here
A gentle introduction to causal reasoning
Tuesday, March 28th
9:30 AM - 6:00 PM

Durand Auditorium
Esclangon Building
Potential outcomes and counterfactuals
DAGs (first pass)
Hands-on exercises
  Definition, identification of causal quantities
Wednesday, March 29th
9:30 AM - 12:30 PM

Board & Seminar room
Esclangon Building
DAGs (second pass)
Causal quantities and corresponding statistical parameters
A preview about inference (G-formula)
Causal inference for mechanisms
Wednesday, March 29th
2:00 PM - 6:00 PM

Board & Seminar room
Thursday, March 30th
9:30 AM - 6:30 PM

Durand Auditorium
Esclangon Building
Interaction, effect modification, causal interaction
Lab 2 exercise sheet, concepts of mediation
Lab 2 exercise sheet, mediation analysis – software application
Poster Session (Thursday afternoon)
Friday, March 31st
9:30 AM - 5:00 PM
Elements of targeted learning
Herpin Auditorium
Esclangon Building


Antoine Chambaz
Paris Cité University
Biography (click to expand/collapse)

Antoine Chambaz is a Professor at Université Paris Cité, member of the applied mathematics laboratory (MAP5). He is the head of the Statistics group since June 2018, and the director of the FP2M research federation. His main research interest is in theoretical, computational and applied statistics.

Caleb H. Miles
Columbia University Mailman School of Public Health

Biography (click to expand/collapse)

Caleb H. Miles is an Assistant Professor in the Department of Biostatistics at the Columbia University Mailman School of Public Health. He works on developing semi-parametric methods for causal inference and applying them to problems in public health. His applied work has largely been in HIV/AIDS, and he has more recently begun to work on psychiatric applications. His current methodological research interests include causal inference, its intersection with machine learning, mediation analysis, interference, and measurement error.

Linda Valeri
Columbia University Mailman School of Public Health

Biography (click to expand/collapse)

Linda Valeri is an Assistant Professor in Biostatistics at the Columbia University Mailman School of Public Health and Adjunct Assistant Professor of Epidemiology at the Harvard T.H. Chan School of Public Health. She joined Columbia Mailman School of Public Health in 2018 after 3 years as faculty at the Laboratory of Psychiatric Biostatistics of McLean Hospital and the Department of Psychiatry at Harvard Medical School. Her research interests are at the intersection of causal inference, biostatistics, epidemiology, and machine learning. She is fond of collaborating with researchers and practitioners in the biomedical and social sciences and of translating statistical methods in Public Health to improve our understanding of mental health, environmental determinants of health, and health disparities. She is the recipient of an NIH Career Development Award to develop causal inference methodology for mobile health studies in Psychiatry.


Antoine Chambaz
Paris Cité University
Marianne Clausel
University of Lorraine
Alessandro Leite
TAU, INRIA, Paris-Saclay University

Organized by

With additional support from

Code of Conduct

Our Spring School on Causality is dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of participants in any form. Sexual language and imagery is not appropriate for any venue, including talks, workshops, parties, Twitter and other online media. Participants violating these rules may be sanctioned or expelled from the event at the discretion of the conference organizers. If you have any concerns about possible violation of the policies, please contact the organizers (organizers.quarter.causality@gmail.com) as soon as possible.