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Causal inference

What would have happened otherwise — and how to defend the answer when nobody can observe it.

  1. Causal Inference as Decision Science
  2. Association, Prediction, Intervention, and Counterfactuals
  3. Potential Outcomes and the Fundamental Problem
  4. Units, Treatments, Versions, and Timing
  5. Estimands, Target Populations, and Effect Scales
  6. Identification Versus Estimation
  7. Causal DAGs and Assumption Maps
  8. Confounders, Mediators, Colliders, and Selection
  9. Consistency, Exchangeability, Positivity, and Interference
  10. Target Trials and Protocol-First Causal Design
  11. Randomization and Intention-to-Treat Effects
  12. Product A/B Testing and Instrumentation
  13. Power, Minimum Detectable Effects, and Sample Size
  14. Cluster-Randomized Experiments
  15. Noncompliance, Encouragement Designs, and LATE
  16. Attrition, Missing Outcomes, and Protocol Deviations
  17. Interference, Spillovers, and Network Experiments
  18. Multiplicity, Subgroups, and Sequential Monitoring
  19. Adaptive Experiments and Bandit Allocation
  20. The G-Formula and Standardization
  21. Outcome Regression and Counterfactual Model Diagnostics
  22. Propensity Scores and Covariate Balancing
  23. Matching and Subclassification
  24. Inverse Probability Weighting and Pseudo-Populations
  25. Overlap, Positivity, Trimming, and Target Redefinition
  26. AIPW, Orthogonal Scores, and Double Robustness
  27. Targeted Maximum Likelihood Estimation
  28. Measurement Error, Proxies, and Misclassification
  29. Missing Data, Selection Bias, and Observation Processes
  30. Sensitivity Analysis, Negative Controls, and Falsification
  31. Time-to-Event Outcomes, Censoring, and Competing Risks
  32. Observational Target-Trial Emulation
  33. Time-Varying Treatments and Treatment–Confounder Feedback
  34. Marginal Structural Models, G-Computation, and G-Estimation
  35. Instrumental Variables Beyond Randomized Encouragement
  36. Regression Discontinuity Designs
  37. Difference-in-Differences and Parallel Trends
  38. Staggered Adoption and Event-Study Designs
  39. Synthetic Control and Synthetic Difference-in-Differences
  40. Interrupted Time Series and Natural Experiments
  41. Heterogeneous Treatment Effects and CATE
  42. Meta-Learners and Uplift Modeling
  43. Causal Trees and Generalized Random Forests
  44. Double/Debiased Machine Learning
  45. Policy Learning and Off-Policy Evaluation
  46. Mediation, Direct Effects, and Mechanism Questions
  47. Transportability and External Validity
  48. Causal Discovery and Causal Representation Learning
  49. Distributional Effects, Equity, and Causal Reporting
  50. Causal Inference Capstone: Design and Defend an Intervention Study