Causal Inference for Time-Varying Treatments
19
Other approaches to causal inference
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
19.1
Targeted Learning
19.2
Instrumental variable analysis
19.3
Regression discontinuity
19.4
Difference-in-Difference
Causal Inference for Time-Varying Treatments
19
Other approaches to causal inference
19
Other approaches to causal inference
19.1
Targeted Learning
rnorm
(
5
)
[1] 1.0362 2.1558 -0.2929 0.8677 0.2885
19.2
Instrumental variable analysis
19.3
Regression discontinuity
19.4
Difference-in-Difference
18
Sensitivity analysis
20
Other approaches to causal inference