Author

Emilie Devijver

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#artificial intelligence Preprint Oct 2026

Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria

Identifying causal effects from observational data typically requires a fully specified causal graph, an assumption that is often unrealistic in complex or high-dimensional settings. Causal abstractions address this limitation by retaining only partial causal information, thereby defining equivalence classes of compati...

Clément Yvernes, Marianne Clausel, Emilie Devijver et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Inferring Causal Relations between Two Sequences of Events with Language Models

It is shown in this study that it is possible to leverage the predictive power of Large Language Models (LLMs) to infer causal relations between only two sequences of events, which provides better results than standard causal discovery algorithms on several time series data, even though these data were converted into s...

Nishchal Prasad, Éric Gaussier, Emilie Devijver et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.

Lei Zan, C. Assaad, Emilie Devijver et al. · 0 citations