10 Identification and the causal hierarchy
- Motivating examples for identification
- Using y0 for identification and deriving estimands
- How to derive counterfactual graphs in y0
- Deriving SWIGs for graph-based counterfactual identification
The practice of advancing machine learning often relies on a blind confidence that more data and the right architecture can solve any task. For tasks with causal elements, causal identification can make that less of a matter of faith and more of a science. It can tell us when more data won’t help, and what types of inductive biases are needed for the algorithm to work.
10.1 The causal hierarchy
10.1.1 Where questions and queries fall on the hierarchy
10.1.2 Where models and assumptions fall on the hierarchy
10.1.3 Where data falls on the hierarchy
10.1.4 The causal hierarchy theorem
10.2 Identification and the causal inference workflow
10.2.1 Defining identification
10.2.2 The causal inference workflow
10.2.3 Separating identification and estimation
10.3 Identification with backdoor adjustment
10.3.1 The backdoor adjustment formula
10.3.2 Demystifying the back door
10.4 Graphical identification with the do-calculus
10.4.1 Demystifying the do-calculus
10.5 Graphical identification algorithms
10.5.2 Demystifying the front door