activity
20212026
most citedCase Studies of Causal Discovery from IT Monitoring Time Series

4 citations · 9 across the 24 of their papers we have counts for

collaborators
Showing 2025 · cs.AIShow all

5 papers · 2 filters

cs.AI2025

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…

cs.AI2025

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…

cs.AI2025

Identifying Macro Causal Effects in C-DMGs over DMGs

Simon Ferreira, Charles K. Assaad

The do-calculus is a sound and complete tool for identifying causal effects in acyclic directed mixed graphs (ADMGs) induced by structural causal models (SCMs). However, in many re…

cs.AI2025★ 1 cited

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.AI2025

Identifying Macro Causal Effects in a C-DMG over ADMGs

Simon Ferreira, Charles K. Assaad

Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods ass…