Each analysis in this atlas is built on the same scaffold. A directed acyclic graph encodes the causal hypotheses; hazard ratios and standardised mean differences from the literature are converted to a common scale; eigenvalue-corrected correlation matrices strip the double-counting that arises when interventions share mechanism.

The framework follows Pearl (2009) and Pearl & Mackenzie (2018), with Chinn's SMD-to-OR transform where trial endpoints differ from the target outcome. Confounders satisfying the backdoor criterion are conditioned on; mediators are not, to avoid blocking the very paths under study. Sensitivity is reported as the E-value: the minimum strength an unmeasured confounder would need to explain away an observed effect. Each entry closes with an antithesis section — what would falsify the inferred causal effect.

Stage	Operation
Graph	Specify DAG G = (V, E) encoding mechanistic hypotheses. Distinguish confounders, mediators, colliders.
Identification	Apply the backdoor criterion to select adjustment set Z such that P(Y | do(X)) = Σz P(Y | X, Z) P(Z).
Effect scaling	Convert SMD → HR via Chinn (2000); aggregate by inverse-variance weighting; flag publication bias.
De-correlation	Eigenvalue-corrected correlation matrix; remove redundant shared-mechanism variance (typical residual ρ̄ ≈ 0.30).
Counterfactuals	Compute Probability of Necessity, Sufficiency, and Necessity-and-Sufficiency per Tian & Pearl (2000).
Robustness	E-value, tipping-point analysis, leave-one-out sensitivity, dose-response saturation modelling.
Antithesis	Explicit counter-argument section: what would falsify the inferred causal effect?