causal-dag-builderlisted

# Causal DAG builder Observational analytics tempts everyone into two mistakes: assuming correlation is cause, and "controlling for everything" to launder it. Both mistakes go away when the assumed causal structure is written down first. A DAG forces the assumptions onto paper, where they can be argued with. This skill emits one, refines it as evidence arrives, and uses the back-door criterion to pick the adjustment set — instead of throwing every available variable into a regression. ## When NOT to use this - The comparison is a properly-randomised A/B test with clean exposure events. Randomisation handles confounding by construction; the DAG adds nothing. Use `experiment-result-reader` instead. - The user is asking a descriptive question ("how many users converted last week?"), not a causal one. Descriptive answers don't need causal structure. - The DAG would have a single arrow (X → Y, no other variables in the system). That's not a DAG, that's an assertion. Either there genuinely are no other variables (rare) or the modeller hasn't thought hard enough yet. - The dataset is so thin that no adjustment set has support. A DAG can tell you which variables to condition on; it cannot conjure rows that aren't there. ## What a DAG is, in 100 words A causal DAG is a directed acyclic graph where **nodes are variables** and **arrows mean "directly causes"** (in the modeller's belief, not in the data). Acyclic = no variable causes itself through a loop. The DAG encodes **assumpti