• 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
  • Published with bookdown

Causal Inference Notes

Reference

Books

  • Causal Inference: What If
  • Causal Inference: The Mixtape
  • Causal Inference for the Brave and True
    • This online book has python code

Courses

  • STA640 Causal Inference

Code

  • Causal Inference: What If. R code