Causal Inference for Time-Varying Treatments
23
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
23.1
Targeted Learning
23.2
Instrumental variable analysis
23.3
Regression discontinuity
23.4
Difference-in-Difference
Causal Inference for Time-Varying Treatments
23
Other approaches to causal inference
23
Other approaches to causal inference
23.1
Targeted Learning
rnorm
(
5
)
[1] 0.37408 1.28246 0.49400 -0.11586 0.08669
23.2
Instrumental variable analysis
23.3
Regression discontinuity
23.4
Difference-in-Difference
22
Target Trial Emulation
References