16 papers
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…
Missing data and cluster graphs: cluster-level missingness vs variable-level missingness
Willow Scott, Eugenio Valdano, Charles Assaad
Missing data is pervasive in many scientific domains such as public health, environmental science, and the social sciences. Recoverability from missing data is typically studied us…
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…
Time Partitioning in Target Trial Emulation
Harold Tankpinou Zoumenou, Simon Ferreira, Charles Assaad +3
In target trial emulation, time partitioning enables researchers to handle time-varying confounders and immortal time bias with appropriate methods. Based on two clinical scenarios…