Causal Foundations
Potential outcomes, structural causal models, DAGs, confounding, colliders and the do-operator.
4concepts
58flashcards
29minutes of reading
- 01 DAGs, Confounders and Colliders A causal graph turns "which variables should I control for" into a question with a mechanical answer, and shows why conditioning on the wrong variable creates bias rather than removing it.
- 02 Potential Outcomes and the Fundamental Problem A causal effect is a comparison of two outcomes for the same unit, only one of which is ever observed, which makes causal inference a missing-data problem rather than a modelling problem.
- 03 Simpson's Paradox and Choosing an Adjustment Set The same data can show an effect in every subgroup and the opposite effect in aggregate, and the arithmetic cannot tell you which is right; only the causal structure can.