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C. K. Assaad. Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion. Transactions on Machine Learning Research. 2025.
Link
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C. K. Assaad. Causal reasoning in difference graphs. The 4th Conference on Causal Learning and Reasoning. 2025.
ArXiv Link (selected for an oral presentation)
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S. Ferreira and C. K. Assaad. Identifying macro conditional independencies and macro total effects in summary causal graphs with latent confounding. The 39th AAAI Conference on Artificial Intelligence. 2025.
Link (selected for an oral presentation)
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B. Glemain, C. K. Assaad, W. Ghosn, P. Moulaire, X. de Lamballerie, M. Zins, G. Severi, M. Touvier, J.F. Deleuze, SAPRIS-SERO Study Group, N. Lapidus, F. Carrat. Revisiting the link between COVID-19 incidence and infection fatality rate during the first pandemic wave. 2025.
Link
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L. Zan, C. K. Assaad, E. Devijver, E. Gaussier, and A. Ait-Bachir. On the fly detection of root causes from observed data with application to IT systems. ACM International
Conference on Information and Knowledge Management. 2024.
Link
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C. K. Assaad, E. Devijver, E. Gaussier, G. Goessler, and A. Meynaoui. Identifiability of total effects from abstractions of time series causal graphs. The 40th Conference on Uncertainty in Artificial Intelligence. 2024.
Link
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D. Bystrova, C. K. Assaad, J. Arbel, E. Devijver, E. Gaussier, and W. Thuiller. Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms. Transactions on Machine Learning Research. 2024.
Link
