5 papers
Topological Residual Asymmetry for Bivariate Causal Direction
Mouad El Bouchattaoui
Inferring causal direction from purely observational bivariate data is fragile: many methods commit to a direction even in ambiguous or near non-identifiable regimes. We propose To…
Learning Causality for Longitudinal Data
Mouad EL Bouchattaoui
This thesis develops methods for causal inference and causal representation learning (CRL) in high-dimensional, time-varying data. The first contribution introduces the Causal Dyna…
Meta-Learning and representation learner: A short theoretical note
Mouad El Bouchattaoui
Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their lear…
Random Walk in Random Environment: A short introduction
Mouad El Bouchattaoui
This is a report of a scientific project carried out at Ecole Centrale Casablanca in 2019. This work is an entry into the world of Random Walk in a Random Environment (RWRE). We wi…
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit +1
Accurately estimating treatment effects over time is crucial in fields such as precision medicine, epidemiology, economics, and marketing. Many current methods for estimating treat…