This school takes place in a thematic quarter that underlines the interactions between Statistical, Probability Theory, Causal Inference theory, and theoretical computer science methods for causal inference. It will enable researchers, students, and practitioners to explore a rich and cross-disciplinary topic. This thematic quarter will explore topics related to, but not limited to:
Causal discovery
Causal learning and control problems
Theoretical foundation of causal inference
Causal inference and active learning
Causal learning in low data regime
Reinforcement learning
Causal machine learning
Causal generative models
Benchmark for causal discovery and causal reasoning
This school particularly focuses on practical aspects, by introducing the main packages available nowadays for causal discovery and causal reasoning.
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