Causal Inference with Models
18
Sensitivity analysis
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
13
Standardization and the Parametric G-Formula
14
G-Estimation of Structural Nested Models
15
Outcome Regression and Propensity Scores
16
Instrumental Variable Estimation
17
Causal mediation analysis
18
Sensitivity analysis
Causal Inference for Time-Varying Treatments
19
Other approaches to causal inference
20
Other approaches to causal inference
21
G-Methods for Time-Varying Treatments
22
Target Trial Emulation
23
Other approaches to causal inference
References
Table of contents
18.1
Quantitative bias analyses
18.2
Tipping point analyses
Causal Inference with Models
18
Sensitivity analysis
18
Sensitivity analysis
18.1
Quantitative bias analyses
rnorm
(
5
)
[1] 1.8480 1.1680 -0.8987 -0.9401 -1.1796
18.2
Tipping point analyses
17
Causal mediation analysis
19
Other approaches to causal inference