Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
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Updated
Aug 11, 2026 - Python
Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
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
Replication materials for Causal Forests versus Penalized Splines for Heterogeneous Treatment Effects
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