A research-grade, 6-week masterclass in Causal Inference and Causal ML from first principles. Rebuilds d-separation oracles, propensity score IRLS engines, doubly-robust AIPW estimators, Cross-Fitting Double Machine Learning (DML), and honest causal forests from scratch in pure NumPy. Fully verified against causal truth

Estimating the causal effects of CIA‑involved coups on institutional quality in Sub‑Saharan Africa using declassified JFK, RFK, and MLK records. Combines panel IV, causal forests, and synthetic control methods

Bayesian Causal Forests — stochtree BCF wrapper, segment CATE, ElasticityEstimator, FCA EP25/2 audit report