One day workshop

Registration is free but mandatory before March 12, 2026 : here 

This event is a joint event between the RT Uncertainty Quantification, the CAUSALITAI project, and the AI clusters MIAI, SCAI and DATAIA 

Speakers

Irène Balleli Centre Inria d'Université Côte d'Azur, équipe/projet EPIONE

Title: Voting, ensembling, and population-level causal discovery: what and how an expert audience can contribute?

Abstract: Discovering reliable cause-and-effect relationships from real-world data is an extremely complex and still open challenge. Existing Causal Discovery (CD) algorithms, even when proven theoretically identifiable, rely on strict assumptions that are rarely met in complex real-world scenarios, such as the functional form of the causal relationships, the data distribution family, and the causal sufficiency. As a result, the reliability of these algorithms can significantly drop, compromising the interpretability of the results and the trustworthiness of downstream decision-making. What if, instead of relying on a single CD expert and its partial understanding of the true underlying causal mechanism, we consulted an audience of experts? In this talk, I will introduce and discuss three main strategies for achieving expert consensus on causal discovery: voting, ensembling, and population-based analysis. I will highlight the level of additional information that each of the considered strategies can provide compared to a traditional single-expert-based approach, and outline how such strategies can be implemented effectively, starting with the communication bottleneck between experts. I will present some results from controlled simulation studies and a real-case application on lung cancer genetic disruptions, which will demonstrate the effectiveness of an expert audience in reinforcing the strengths of each expert while mitigating their uncertainties.

Victor Elvira University of Edinburgh

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Agathe Fernandes Machado  UQAM, Montreal (Quebec, Canada) 

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Zhe Li Université de Bordeaux

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Aurore Lomet CEA Saclay LIAD 

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Preben Ness Simula Research Laboratory (Oslo, Norway)

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Margaux Zaffran  Institut Mathématiques d'Orsay, équipe/projet Inria Celeste

Titre : Momentum Smooths the Path to Gradient Equilibrium

Résumé : Online gradient descent has recently been shown to satisfy gradient equilibrium for a broad class of loss functions, including quantile loss and squared loss. This means that the average of the gradients of the losses along the sequence of estimates converges to zero, a property that allows for quantile calibration and debiasing of predictions, among other useful properties of statistical flavor. A shortcoming of online gradient descent when optimized for gradient equilibrium is that the sequence of estimates is jagged, leading to volatile paths. In this work, we propose generalized momentum method, in the form of weighting of past gradients, as a broader algorithmic class with guarantees to smoothly postprocess (e.g., calibrate or debias) predictions from black-box algorithms, yielding estimates that are more meaningful in practice. We prove it achieves gradient equilibrium at the same convergence rates and under similar sets of assumptions as plain online gradient descent, all the while producing smoother paths that preserve the original signal amplitude. Of particular importance are the consequences for sequential decision-making, where more stable paths translate to less variability in statistical applications. These theoretical insights are corroborated by real-data experiments, showcasing the benefits of adding momentum.

Qingyuan Zhao University of Cambridge

Title: A counterfactual perspective of heritability, explainability, and ANOVA

Abstract: Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. Motivated by the concept of genetic heritability in twin studies, this talk will introduce a new notion called counterfactual explainability for causal attribution. This can be viewed as an extension of global sensitivity analysis (functional ANOVA and Sobol’s indices), which assumes independent explanatory variables, to dependent explanatory variables whose causal relationship can be described by a directed acyclic graph. The new notion will be illustrated using several artificial and real-world examples. This talk is based on joint works with Zijun Gao, Haochen Lei, and Hongyuan Cao.

Scientific comitee

Marianne Clausel, Emilie Chouzenoux, Emilie Devijver and Clémentine Prieur