3 papers
cs.AI2025
Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Lucas de Lara
Counterfactual reasoning aims at answering contrary-to-fact questions like ``Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of c…
stat.ML2025
What is a good matching of probability measures? A counterfactual lens on transport maps
Lucas De Lara, Luca Ganassali
Coupling probability measures lies at the core of many problems in statistics and machine learning, from domain adaptation to transfer learning and causal inference. Yet, even when…
stat.ML2025
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning
Lucas de Lara, Mathis Deronzier, Alberto González-Sanz +1
The push-forward operation enables one to redistribute a probability measure through a deterministic map. It plays a key role in statistics and optimization: many learning problems…