treatment-effects 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
ALICE（因果关系和经济学自动学习和智能）是微软研究中心的一个项目，旨在将人工智能概念应用于经济决策。其目标之一是构建一个将最先进的机器学习技术与计量经济学相结合的工具包，以便将自动化解决复杂的因果推理问题。到目前为止，ALICE Python SDK（econml）实现了诸如Sirizhukov等人的双机器学习工作之类的垂直机器学习算法。该工具包旨在测量某些治疗变量t对结果变量y的因果影响，控制一组特征x。
Must-read papers and resources related to causal inference and machine (deep) learning
Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.
StatsPRI是第一个用于因果推理和应用计量经济学的代理原生Python库-统一API、广泛的跨方法覆盖范围、结构化结果对象、机器可读模式、技能、HCP服务器和R/Stata平价验证。
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
Automated Double Machine Learning (DoubleML + AutoML) pipeline for causal inference — estimate incremental impact, CATE & ROI of promotions, pricing and media.
RD设计的鲁棒局部多项方法
OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
A General Causal Inference Framework by Encoding Generative Modeling
Machine learning based causal inference/uplift in Python
R package for Bayesian meta-analysis models, using Stan
该项目在 GitHub 上没有填写描述
This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.
Methods for subgroup identification / personalized medicine / individualized treatment rules
My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context
Lightweight uplift modeling framework for Python
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning