Identifiability

In causal inference, a causal query — most commonly an interventional distribution P(Y | do(X = x)) — is identifiable from observational data over a variable set V iff it can be written as an expression in the observational distribution P(V). Identifiability is a graphical property: it depends only on the causal DAG, not on the parametric form of the distribution. Pearl’s Backdoor Criterion and Front-door Criterion give sufficient graphical conditions; Shpitser–Pearl 2006’s ID algorithm is sound and complete for identification — it finds an expression if and only if one exists. The Rubin / potential-outcomes counterpart uses the Ignorability + Positivity conditions to characterise when standardisation by covariates yields the average treatment effect. For CIVeX-style certificates, identifiability is the property the certificate establishes — the prover ships the DAG, assumptions, and ID derivation; the host checks the derivation rather than re-running the algorithm.

In this vault

• Causal Diagrams for Empirical Research - Pearl
• Causality - Pearl
• Identification of Joint Interventional Distributions - Shpitser Pearl
• Potential Outcomes Framework - Rubin
• do-Calculus
• Backdoor Criterion
• Front-door Criterion
• Causal DAG
• Structural Causal Model
• Causal Inference
• Counterfactual
• CBCL - Safe Self-Extending Agent Communication