Math for Causal Inference
PHASE 6 · DAYS 81–100

Advanced & Algorithmic Causal Inference

Matching and balancing, synthetic control, influence functions, double/debiased ML, heterogeneous effects and policy learning.

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  1. 81
    Matching Estimators & Their Bias
    Coming soon
  2. 82
    Propensity-Score Matching & Weighting: Proofs
    Coming soon
  3. 83
    Covariate Balance, Entropy & KL Divergence
    Coming soon
  4. 84
    Entropy Balancing & Calibration Weights
    Coming soon
  5. 85
    Synthetic Control: the Weight Problem
    Coming soon
  6. 86
    Synthetic Control Inference & Synthetic DiD
    Coming soon
  7. 87
    Influence Functions
    Coming soon
  8. 88
    Semiparametric Efficiency Bounds
    Coming soon
  9. 89
    Neyman Orthogonality
    Coming soon
  10. 90
    Double/Debiased Machine Learning & Cross-Fitting
    Coming soon
  11. 91
    DML for Partially Linear & Interactive Models
    Coming soon
  12. 92
    CATE & Meta-Learners (S, T, X, R, DR)
    Coming soon
  13. 93
    Causal Trees & Forests
    Coming soon
  14. 94
    Policy Learning & Optimal Treatment Rules
    Coming soon
  15. 95
    Sensitivity Analysis (Rosenbaum, Cinelli–Hazlett)
    Coming soon
  16. 96
    Dynamic Treatments & Markov Processes
    Coming soon
  17. 97
    Interference & Spillovers
    Coming soon
  18. 98
    Causal Discovery Basics
    Coming soon
  19. 99
    Many Estimates: Multiple Testing & Shrinkage
    Coming soon
  20. 100
    Final Capstone: From Data to a Defensible Causal Estimate
    Coming soon