activity
20242026
collaborators

7 papers

cs.AI2026

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…

stat.ME2026

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…

stat.ME2025

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…

cs.AI2025

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…

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…

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…