causal-evidence-checklistlisted

# Causal evidence checklist Observational analytics is full of correlations that look causal and aren't. A deploy ships Tuesday, bounce rate jumps Wednesday, and the instinct is to roll back. Sometimes the deploy did it. Sometimes a marketing campaign landed the same day. Sometimes Wednesday is always like that. This skill encodes a 60-year-old epidemiology rubric — Bradford Hill's 9 viewpoints (1965) — as a checklist the agent fills before recommending an action. Hill's original audience was epidemiologists deciding whether smoking caused lung cancer without the option of a randomized trial. The same constraint applies to most product analytics: you can't randomize a deploy across a population, so you reason from observational evidence and triangulate. The 9 viewpoints are how. ## When NOT to use this - **The evidence is from a properly-randomized A/B test.** Randomization handles most of these criteria automatically (temporality, specificity, confounding). Use `experiment-result-reader` instead. The checklist is for observational data where you can't randomize. - **The user only wants an exploratory hypothesis, not a decision.** This skill gates recommendations. If they're brainstorming what *might* explain a chart and are nowhere near acting, it's overkill — use `analytics-diagnostic-method` to build the hypothesis tree first. - **The metric move is inside noise.** If the "effect" is 1pp on n=200, there's nothing to explain yet. Send the user back to sample-size discip