Each case opens with the population-level statistic that looked authoritative and walks through what the causal model showed about the individual decision underneath. Model files ship with every case.

Statins & Hospitalisation → The correct average applied to the wrong individual.

A policy that works on average is applied uniformly — including to patients for whom it is harmful, neutral, or unnecessary. Population statistics used to make individual decisions.

Iatrogenic Medications → The statin isn’t fighting the disease. In many cases, it’s fighting another drug.

An audit attributed a large share of adverse events to underlying conditions. The causal model found they were drug-drug interactions in the prescribing cascade. The reserve was overstated; the clinical liability was understated.

Adverse Event Attribution → The NSAID raises AKI risk in the population. Did it cause this patient’s AKI?

A pharmacovigilance signal is a population-level association. The clinical and legal question is whether this exposure caused this harm. Two different questions, two different formal structures.

Sepsis Dynamic Treatment Regimes → The vasopressor was given to the patients who needed it — so observational data shows it kills.

Treatment confounded by indication is the classic Rung 1 trap. The covariates that selected for treatment also selected for outcome; standard adjustment cannot reach the counterfactual. A dynamic regime model can.

Immunotherapy vs Chemotherapy → The biomarkers that select the patient also select the response.

Selection effects make immunotherapy look better than it is in head-to-head observational comparisons. The model separates selection from effect — the patients who would have responded to chemo, do.

Drug Repurposing — Transportability → The trial population is not the target population.

A drug’s effect estimated in one population does not transport unaltered to another. The transportability formula closes the gap when the causal structure is known and the differences are characterised — and is silent on what to do when they aren’t.

Treatment-Resistant Depression → After two failed antidepressants, “what works on average” is no longer the right question.

The patient has demonstrated what doesn’t work for them. The next decision is a Rung 3 counterfactual: which option, on this individual, given their non-response pattern. Population statistics applied to a non-representative individual.

For the methods behind these cases, see Causal Modeling, Pearl’s Ladder (population vs. individual reasoning), and Medicine (the cluster taxonomy with sub-branches). For the wider portfolio across all five risk types, see About Risk.