Causal Inference without Models
9
Measurement Bias and "Noncausal" Diagrams
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
9.1
Measurement error
9.2
The structure of measurement error
9.3
Mismeasured confounders and colliders
9.4
Causal diagrams without mismeasured variables?
9.5
Many proposed causal diagrams include noncausal arrows
9.6
Does it matter that many proposed diagrams include noncausal arrows?
9
Measurement Bias and “Noncausal” Diagrams
rnorm
(
5
)
[1] 1.61416 1.17402 1.02173 0.05513 0.84015
9.1
Measurement error
9.2
The structure of measurement error
9.3
Mismeasured confounders and colliders
9.4
Causal diagrams without mismeasured variables?
9.5
Many proposed causal diagrams include noncausal arrows
9.6
Does it matter that many proposed diagrams include noncausal arrows?
8
Selection Bias
10
Random Variability