SCOR Foundation for Science Webinar, ML and Econometrics

This week, I will give a talk at the SCOR Foundation for Science webinar (slides are available online). and I was asked to give a talk on econometrics vs IA (or machine learning),

Of course, the two concepts are related, and there is a continuum between them.

As we wrote it Charpentier et al. (2017)

Econometrics and machine learning seem to have one common goal: to construct a predictive model, for a variable of interest, using explanatory variables (or features).

For the purposes of this presentation, we will begin by contrasting the two, emphasizing the differences, and then showing the connections that exist.

Long story short, in between, we have computation statistics, or statistical learning, corresponding to computational techniques with mathematical probabilistic guarantees.

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Causalité, pour l’Institut des Actuaires, Paris

Ce mardi, je participe en visio à la conférence annuelle de l’Institut des Actuaires, à Paris, pour une session sur les modèles causaux en assurance, avec une introduction générale, avant qu’Aurélien Couloumy ne prenne la suite pour présenter des applications.

Pour ceux qui veulent un exercice pour l’été, je peux mentionner un tableau tiré de “Optimum Strategies for Creativity and Longevity

Si quelqu’un arrive à établir un lien causal, je suis intéressé.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models

Our paper Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models, with Marouane Il Idrissi and Agathe Fernandes Machado, is now online. It will be presented at the IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI), in Montréal this Summer…

Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient allocations, the Weber and Harsanyi sets, that extend beyond Shapley values and offer richer interpretative flexibility. We present an accessible overview of these allocation schemes, clarify the distinction between value functions and aggregation rules, and introduce a three-step blueprint for constructing reliable and theoretically-grounded feature attributions. Our goal is to move beyond fixed axioms and provide the XAI community with a coherent framework to design attribution methods that are both meaningful and robust to shifting methodological trends.