Alcohol-use disorder (AUD) affects roughly 29 million United States adults and is a leading cause of preventable death through liver disease, cardiovascular disease, cancer, and injury. This oracle estimates the causal reduction in relapse to heavy drinking — the proximal mediator of alcohol-related mortality — achievable by combining treatments, using effect sizes from named trials and meta-analyses. Hard-mortality endpoints are largely observational and flagged accordingly.
Tick the interventions to combine. Each shows its trial hazard ratio, 95% confidence interval (CI), E-value, mechanism, and citation. ★ = in the current Pareto effective set but not yet ticked.
Headline is the front-door estimate: shared drinking-reduction-mediator overlap removed via dose-response saturation; residual direct-effect overlap removed via the eigenvalue model at ρ̄.
Under monotonicity + exogeneity (E-value bounds the exogeneity assumption).
Faithful causal directed acyclic graph (DAG). Each treatment is a distinct node; nearly all effects flow through one shared mediator — reduction in alcohol consumption — which lowers liver disease, cardiovascular disease and injury/accidents, each a route to mortality. Disulfiram carries a competing-harm path (disulfiram-ethanol reaction / hepatotoxicity). Named confounders open back-door paths (adjusted). Illustrative of structure, not yet the identification engine.
Eigenvalue diagnostics for the selected interventions under an equicorrelation matrix (off-diagonal ρ̄). A large λmax relative to k signals redundancy; n_eff is the effective number of independent interventions actually contributing.
On mechanistic grounds ρ̄ ≈ 0.30 is defensible for this domain: incretin drugs, surgery, diet, and activity overwhelmingly share the weight-loss / insulin-sensitivity pathway, so ~30% of their nominal effects overlap. A mediation analysis of semaglutide found ~80% of its MACE benefit is not mediated by weight loss, which is why ρ̄ is user-adjustable rather than fixed.
Minimum-effective-set analysis. Set a target combined risk reduction; the model finds the smallest set of interventions — accounting for front-door mediator overlap — that reaches it, and highlights them. If the target exceeds what all interventions together can achieve, the full set is shown (never an empty one). "Apply" ticks exactly that set.
Monte Carlo propagation. Each selected intervention's log-hazard-ratio is sampled from a normal distribution implied by its 95% CI; samples are combined with the same eigenvalue overlap discount. 5,000 draws.
Intervening on the selected set S with Pearl's do-operator (setting the interventions, not merely observing them). Contrast against do(∅) = no intervention.
For each intervention: "if not for this one, the combined front-door hazard ratio would be…". Isolates each intervention's marginal causal contribution after mediator-overlap removal, so shared-pathway agents are not double-credited.
| If not for… | HR without it | HR with full set | marginal RRR lost |
|---|
One-at-a-time sensitivity. Each intervention's hazard ratio is swung across its 95% confidence interval (others held at point estimate); the bar is the resulting swing in the combined front-door HR. A long bar means the combined estimate leans heavily on that single trial's precision.
Front-door (mediation) decomposition. Nearly every AUD treatment acts through one shared mediator — reduction in alcohol consumption. Because the mechanism is drinking reduction, the mediated fraction is high (~0.85\u20130.95): each log-effect is split into a drinking-mediated (indirect) and a small drinking-independent (direct) part. Indirect parts are pooled through the mediator with dose-response saturation (you cannot reduce the same drinking twice), removing the mediator cross-correlation; direct parts keep the residual eigenvalue correlation at ρ̄. The practical consequence: stacking naltrexone + acamprosate + therapy does not multiply abstinence \u2014 it saturates toward it.
| Intervention | HR | %drink↓ | drink-med | indirect logHR | direct logHR |
|---|
Which % of cross-correlation is appropriate? Not one number. The mediator overlap is fixed empirically by the drinking-reduction saturation (currently removing — of the summed drinking-mediated effect when treatments are stacked). Because AUD interventions share almost the entire causal pathway, this overlap is large \u2014 the defining feature of the domain, not a modelling artefact. Drinking-mediated fractions are transparent, adjustable priors.
Front-door caveat (antithesis): the cited endpoints are relapse / heavy drinking \u2014 a surrogate. Mortality benefit is inferred from the drinking→mortality link (heavy drinkers all-cause HR ~1.88), which is itself confounded by the sick-quitter effect (people quit because they are ill: former-drinker HR 1.74). No randomised trial has demonstrated an AUD-drug mortality reduction; indirect estimates are bounds.
Select interventions to generate a plain-language summary.