cate

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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)

Official code for "Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes" EMBC 2025.

CP-plots in R for CATE-propensity diagnostics, bracketing relationships, and weighted treatment effects (ATT, ATO, ATC).

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

Causal Inference in Healthcare: Treatment Effect Analysis

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

Does swapping EconML's auto first stage for CatBoost improve CATE in Double Machine Learning? Same CausalForestDML second stage across 13 datasets, paired Wilcoxon. Auto wins PEHE on 7 of 13; tuned CatBoost wins on smooth DGPs.