Causal inference notes
Preface
5. Interaction
5.1 Interaction requires a joint intervention
5.2 Identifying interaction
5.3 Counterfactual response types and interaction
5.4 Sufficient causes
5.5 Sufficient cause interaction
5.6 Counterfactuals or sufficient-component causes?
6. Graphical representation of causal effects
6.1 Causal diagrams
Causal directed acyclic graphs
Examples
6.2 Causal diagrams and marginal independence
6.3 Causal diagrams and conditional independence
Appendix A: uncorrelated vs. independent
Appendix B: The flow of association and causation in graphs
Graph terminology
Bayesian networks
Causal graphs
Two-node graphs and graphical building blocks
Chains and forks
Colliders and their descendants
d-separation
11. Why model?
Some concepts and points
Program 11.1
Program 11.2
Program 11.3
12. IP weighting and marginal structural models
Part 1: Summary of the chapter
Definitions
0.0.1
More about conditional exchangeability assumption
Equivalence of IP weighting and standardization
Equivalence of potential outcome mean, standardized mean and IP weighted mean
What does IP weighting mean?
An example
Horvitz-Thompson estimator and Hajek estimator
Stablized IP weights
Marginal structural models
Effect modification and marginal structural models
Part 2: Real data analysis
Background
Input dataset
Ignore subjects with missing values for weight in 1982
Compare the treatment group and the control group
Estimating IP weights via modeling
Stablized IP weights
Marginal structural models
Doubly robust estimators
Matching
Matching methods for causal inference: a review and a look forward
Reference
Books
Courses
Code
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Causal Inference Notes
Doubly robust estimators
See notes in overleaf.