causal-query-classifierlisted

# Causal query classifier Most analytics arguments lose at the question, not at the data. Someone shipped a new pricing page, CVR went up the same week, and the deck says "the page lifted CVR by 18%." The data says nothing of the sort — it says CVR was higher the week after launch. Pearl's three-rung causal hierarchy gives you a vocabulary for catching that slide before it happens. This skill makes the rung explicit. Every question is classified before it's answered. Rung-1 questions get rung-1 answers. Rung-2 questions get either a real identification strategy or a refusal to make the claim. ## When NOT to use this - The user is asking a purely descriptive question with no decision attached: "what's our checkout CVR this month?". That's rung-1 by construction; classification is overhead. Just answer it. - A randomized experiment is already running and you're reading its result. Randomization handles identification; load `experiment-result-reader` instead. - The user wants help designing an experiment. Use experiment-design tooling; this skill is for interpreting questions, not specifying tests. - You're inside a forecasting task (rung-1 prediction of the future), not a causal one. Predictions are rung-1; "what would the metric have been if we'd done X instead" is rung-3. ## Background: Pearl's three rungs in plain language Judea Pearl's hierarchy ranks queries by what they require of the data. Each rung subsumes the one below. ### Rung 1 — Association: P(Y | X) What