Causal Foundations
Potential outcomes, structural causal models, DAGs, confounding, colliders and the do-operator.
7concepts
137flashcards
54minutes 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 Identification Assumptions: Exchangeability, Positivity and Consistency The three conditions that let an observed conditional mean stand in for an unobserved potential outcome, how each one enters the identification proof, and which of them data can and cannot check.
- 03 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.
- 04 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.
- 05 Mediation Analysis: Direct and Indirect Effects Splitting a total effect into the part that runs through a mediator and the part that does not requires counterfactuals that mix two worlds, which is why the decomposition needs assumptions that even a randomised experiment cannot deliver.
- 06 Structural Causal Models and Counterfactuals A structural causal model assigns every variable a mechanism and an exogenous noise term, which is exactly the extra structure needed to answer "what would have happened to this unit", the question neither correlations nor interventions can answer.
- 07 The Do-Operator and Identification The distinction between conditioning on what you observed and intervening to set a value, and why identification is a question about the graph that must be settled before any estimation.