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Causal inference across time
Causal Inference in R
Preface
Asking Causal Questions
1
From casual to causal
2
The whole game: mosquito nets and malaria
3
Estimating counterfactuals
4
Target trials and standard methods
5
Expressing causal questions as DAGs
6
Causal inference is not (just) a statistical problem
The Design Phase
7
Preparing data to answer causal questions
8
Building propensity score models
9
Using the propensity score
10
Evaluating your propensity score model
Estimating Causal Effects
11
Causal estimands
12
Fitting the outcome model
13
Continuous exposures
14
Categorical exposures
15
G-computation
16
Interaction
17
Missingness
18
Causal inference across time
19
Causal survival analysis
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Causal mediation analysis
21
Sensitivity analysis
22
Machine learning and causal inference
23
Instrumental variables and friends
24
Evidence
References
Table of contents
18.1
Loss to follow-up
18.2
Time-varying confounding and exposures
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18
Causal inference across time
18.1
Loss to follow-up
rnorm
(
5
)
[1] -0.001399 0.308378 -1.366570 -2.147339 -0.179935
18.2
Time-varying confounding and exposures
17
Missingness
19
Causal survival analysis