Glossary · Marketing Engineering
Causal Discovery
also: PC algorithm · constraint-based causal discovery
Definition
Causal discovery is the family of algorithms that infer directed causal structure from observational data alone, using conditional-independence tests (PC, FCI) or score-based search (GES). Applied to business data it produces testable DAGs that replace ad-hoc causal intuitions with falsifiable hypotheses.
Unlike econometric methods that estimate effects given an assumed causal graph, causal discovery learns the graph itself. The PC algorithm (Spirtes, Glymour, Scheines) iteratively tests conditional independence between variable pairs; edges are removed when a separating set is found, orientation follows from collider patterns. FCI relaxes the causal-sufficiency assumption, handling latent confounders. Output is a DAG (or PAG) that constrains which interventions are identifiable and which require experiments.
Essays on this concept
- Business Analytics
Causal Discovery in Business Data: Applying PC Algorithm and FCI to Find Revenue Drivers Without Experiments
Correlation tells you that feature usage and retention move together. It doesn't tell you which causes which — or whether a third factor drives both. Causal discovery algorithms can untangle this from observational data alone.
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