#计算机科学#DoWhy是微软开发的一个用于因果推断的Python库，旨在引发因果关系思考和分析
#计算机科学#ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goal...
#计算机科学#Must-read papers and resources related to causal inference and machine (deep) learning
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
#计算机科学#Implementation of paper DESCN, which is accepted in SIGKDD 2022.
Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
#计算机科学#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 pac...
A General Causal Inference Framework by Encoding Generative Modeling
#计算机科学#Machine learning based causal inference/uplift in Python
R package for Bayesian meta-analysis models, using Stan
Methods for subgroup identification / personalized medicine / individualized treatment rules
#计算机科学#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
#计算机科学#Lightweight uplift modeling framework for Python
#计算机科学#Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
#计算机科学#BITES: Balanced Individual Treatment Effect for Survival data
CRAN Task View: Causal Inference
#计算机科学#Deep Treatment Learning (R)
#计算机科学# 🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions