The general research objectives of the CIPHOD team are to put forth novel theoretical findings and develop innovative methodologies in the realm of causal inference, with a focus on their applicability and utility for epidemiologists. These objectives are centered around three axes while placing particular importance on temporal data and high-level background knowledge (abstractions). The three axes are: discovering causal graphs, identifying and estimating total and direct effets, and searching for root causes of anomalies.
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
C. K. Assaad. Causal reasoning in difference graphs. The 4th Conference on Causal Learning and Reasoning. 2025. ArXiv Link (selected for an oral presentation)
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)
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
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
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
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
S. Ferreira and C. K. Assaad. Identifying macro conditional independencies and macro total effects in summary causal graphs with latent confounding. CI4TS Workshop at The 40th Conference on Uncertainty in Artificial Intelligence. 2024. Link
T. Loranchet and C. K. Assaad. Local Markov Equivalence and Local Causal Discovery for Identifying Controlled Direct Effects. 2025. Link
S. Ferreira and C. K. Assaad. Identifying Macro Causal Effects in C-DMGs. 2025. Link
F. Baldo, S. Ferreira and C. K. Assaad. Discovering maximally consistent distribution of causal tournaments with Large Language Models. 2024. Link
S. Ferreira and C. K. Assaad. Average controlled and average natural micro direct effects in summary causal graphs . 2024. Link
D. Bystrova, C. K. Assaad, S. Si-moussi, and W. Thuiller. Causal discovery from ecological time series with one timestamp and multiple observations. 2024. Link