1 citations · 1 across the 7 of their papers we have counts for
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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…
Regret-Based Federated Causal Discovery with Unknown Interventions
Federico Baldo, Charles K. Assaad
Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data. Recent work has extended these m…
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…
Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases
Federico Baldo, Simon Ferreira, Charles K. Assaad
Traditional causal discovery methods often depend on strong, untestable assumptions, making them unreliable in real-world applications. In this context, Large Language Models (LLMs…
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…
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs
Simon Ferreira, Charles K. Assaad
In this paper, we investigate the identifiability of average controlled direct effects and average natural direct effects in causal systems represented by summary causal graphs, wh…