Prediction, causal intervention and identifiability
Research context
postgraduate concept · established · As of 2026-10-10
Observational conditioning p(Y|X=x) and intervention p(Y|do(X=x)) describe different operations.
Key knowledge
Observational conditioning p(Y|X=x) and intervention p(Y|do(X=x)) describe different operations. In a causal DAG, intervention replaces the equation/distribution generating X. A valid back-door adjustment set yields an observational formula for the intervention under the causal assumptions; unobserved confounding or incompatible models can prevent identification. Better prediction does not resolve this lack of causal identifiability.
Values and equations
p(y|do(x))=sum_z p(y|x,z)p(z), for a valid back-door set Z
- Z
- Covariate set satisfying the back-door criterion
- do(x)
- Intervention setting X to x
- p
- Population law under the stated causal model
Conditions and scope
- Use a causal structural model with declared graph, consistency, population and adjustment assumptions.
- An adjustment set must block the relevant back-door paths and exclude descendants; it is not selected solely by predictive fit.
Sources · 1
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Author checked this English statement, displayed equation where present and stated hypotheses against these source locations: DAG factorization, d-separation, interventions, back-door adjustment and identifiability. The check is bounded to the recorded claims; no complete proof reconstruction, software proof replay or universal literature completeness is claimed.
2026-10-10 · Author source-body/statement/equation/domain check, with graph associations scoped individually. Separate independent content/source/inventory review is required before branch closure.
Entry details
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- research-info-causal-identifiability
- Revision
- research.1
- Structured reference record
- Postgraduate & research reference