6 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…
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…
Causal Contrastive Learning for Counterfactual Regression Over Time
Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit +1
Estimating treatment effects over time holds significance in various domains, including precision medicine, epidemiology, economy, and marketing. This paper introduces a unique app…
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…