causal-dag-builder

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 assumptions, not facts; it is the modeller's hypothesis about the data-generating process, drawn so others can attack it. The point is not the picture. The point is that once the structure is explicit, the back-door criterion mechanically tells you which variables to condition on to estimate a causal effect — and, just as important, which variables you must not condition on.

The three structural patterns

Every triple of nodes in a DAG is one of three shapes. Conditioning rules differ for each.