cate

😉【Android分页菜单控件】快速实现美团、饿了么、京东、淘宝首页分页菜单效果

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.

Official repository for "Composable Causality" (NeurIPS 2026): A modular generator and benchmark for time-series causal discovery, treatment-effect estimation, and exact counterfactuals.

Code related to the Tutorial Paper on SHAPs for meta learners

UpliftForge · 世界顶级异质处理效应/增益建模(CATE)系统：交叉拟合 X/DR/R-learner + 因果森林 + CATEFuse Super-Learner。纯 NumPy 零依赖·确定性·可复现。作者：晨星

Stata package for causal forests / generalized random forests (Wager-Athey 2018; Athey-Tibshirani-Wager 2019): conditional average treatment effects (CATE).

Causal Inference in Healthcare: Treatment Effect Analysis

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.

Deep causal CATE (DragonNet, TARNet, T/S-Learner) with TMLE targeted regularization for retail promo targeting — Qini-optimized, budget-aware.

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

A causal ML engine for uplift modeling, treatment effect estimation, intelligent customer targeting, and profit-driven policy evaluation.

OMOP CDM research repository extending FastOMOP with an NL2SQL-connected causal inference workflow for target trial emulation, literature-grounded DAGs, CATE estimation, and public dataset validation.