Causal Foundations
Potential outcomes, structural causal models, DAGs, confounding, colliders and the do-operator.
7concepts
137flashcards
54minutes of reading
- 01 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.
- 02 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.
- 03 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.