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Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

An Easy and Modern Build System For C/C++ With Readable Syntax.

Code for causal isotonic calibration for heterogeneous treatment effects (appeared in ICML, 2023)

A small public bundle of spatial AI canvas patterns, offered as a token of appreciation to Cate’s builders.

Code related to the Tutorial Paper on SHAPs for meta learners

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

Causal Inference in Healthcare: Treatment Effect Analysis