How do causal diagrams (DAGs) help you choose what to control for?
PICTURE THIS: OVERFITTING
Simple meaning
A DAG encodes assumed causal directions so you can find back-door paths that must be blocked and colliders that must not be conditioned on.
WHY — Correlation vs Causation instead of guessing?
Why interviewers care about Correlation vs Causation:
question about Correlation vs Causation.
trade-offs, and what you would actually do on a Data Science project - not buzzwords.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1A DAG encodes assumed
causal directions so you can find back-door paths that must be blocked and colliders that must not be conditioned on.
- 2Adjusting for mediators or
colliders can induce bias even if those variables correlate with the outcome.
- 3The graph makes the
identifying assumption inspectable instead of a kitchen-sink regression.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
A DAG encodes assumed causal directions so you can find back-door paths that must be blocked and colliders that must not be conditioned on. Adjusting for mediators or colliders can induce bias even if those variables correlate with the outcome.