3 papers
econ.EM2026
A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
Guillaume Bied, Philippe Caillou, Bruno Crépon +3
Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives…
cs.LG2025
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Shuyu Dong, Michèle Sebag, Kento Uemura +4
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DC…
stat.ML2024
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau +2
This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational dat…