Causal Inference without Models
10
Random Variability
Causal Inference: What If
Introduction: Towards Less Causal Causal Inferences
Causal Inference without Models
1
A Definition of Causal Effect
2
Randomized Experiments
3
Observational Studies
4
Effect Modification
5
Interaction
6
Graphical Representation of Causal Effects
7
Confounding
8
Selection Bias
9
Measurement Bias and “Noncausal” Diagrams
10
Random Variability
Causal Inference with Models
11
Why Model?
12
IP Weighting and Marginal Structural Models
Table of contents
10.1
Identification versus estimation
10.2
Estimation of causal effects
10.3
The myth of the super-population
10.4
The conditionality “principle”
10.5
The curse of dimensionality
10
Random Variability
10.1
Identification versus estimation
10.2
Estimation of causal effects
10.3
The myth of the super-population
10.4
The conditionality “principle”
10.5
The curse of dimensionality
9
Measurement Bias and “Noncausal” Diagrams
11
Why Model?