Causal discovery
- Aims to infer the underlying causal structure (often a Directed Acyclic Graph) from data using conditional independencies, scores, or stability assumptions.
- A set of algorithms that attempt to uncover causal graphs from observational data using conditional independence, score-based optimization, or asymmetries.
Overlap: Informs Adjusted Methods & Quasi-experiments (by discovering adjustment sets), supports Causal ML (by identifying variable roles), and relates to Alternative Paradigms when standard graph assumptions are relaxed
Key feature: Infers causal structure
Usage: For learning causal DAGs, discovering/detecting exposure pathways