4 citations · 9 across the 24 of their papers we have counts for
7 papers · 1 filter
On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphs
Isabela Belciug, Simon Ferreira, Charles K. Assaad
Observational studies in fields such as epidemiology often rely on covariate adjustment to estimate causal effects. Classical graphical criteria, like the back-door criterion and t…
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…
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…
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…
Identifiability by common backdoor in summary causal graphs of time series
Clément Yvernes, Charles K. Assaad, Emilie Devijver +1
The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed fro…
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…
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…