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Machine learning and causal inference
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
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Using the propensity score
10
Evaluating your propensity score model
Estimating Causal Effects
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Causal estimands
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Fitting the outcome model
13
Continuous exposures
14
Categorical exposures
15
G-computation
16
Interaction
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Missingness
18
Causal inference across time
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Causal survival analysis
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Causal mediation analysis
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Sensitivity analysis
22
Machine learning and causal inference
23
Instrumental variables and friends
24
Evidence
References
Table of contents
22.1
Prediction and causal inference, again
22.2
Augmented propensity scores
22.3
Targeted Learning
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22
Machine learning and causal inference
22.1
Prediction and causal inference, again
22.2
Augmented propensity scores
rnorm
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5
)
[1] -1.58749 1.51042 -0.02759 -1.44445 0.23216
22.3
Targeted Learning
21
Sensitivity analysis
23
Instrumental variables and friends