17
Missingness
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
20
Causal mediation analysis
21
Sensitivity analysis
22
Machine learning and causal inference
23
Instrumental variables and friends
24
Evidence
References
Table of contents
17.1
Missingness as structural bias
17.2
Multiple Imputation
17.3
Combining MICE and IPW
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17
Missingness
17.1
Missingness as structural bias
rnorm
(
5
)
[1] -0.1366 0.5415 1.2617 1.4822 1.3537
17.2
Multiple Imputation
17.3
Combining MICE and IPW
16
Interaction
18
Causal inference across time