3 papers
cs.AI2026
Root cause analysis via difference graph discovery from linear time-series data
Anouk Ruer, Timothée Loranchet, Daria Bystrova +1
Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series throug…
cs.AI2026
Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects
Timothée Loranchet, Charles K. Assaad
Identifying controlled direct effects (CDEs) is crucial across numerous scientific domains. While existing methods can identify these effects from causal directed acyclic graphs (D…
cs.AI2026
Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
Timothée Loranchet, Charles K. Assaad
Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full…