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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 ...
Created
2018-05-31
1,059 commits to main branch, last one 19 hours ago
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...
Created
2018-04-30
514 commits to main branch, last one 5 days ago
Must-read papers and resources related to causal inference and machine (deep) learning
Created
2019-10-13
81 commits to master branch, last one about a year ago
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
Created
2021-02-18
77 commits to main branch, last one about a year ago
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
Created
2019-12-06
14 commits to master branch, last one 3 years ago
Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
Created
2020-08-07
97 commits to master branch, last one 27 days ago
A General Causal Inference Framework by Encoding Generative Modeling
Created
2022-06-07
238 commits to main branch, last one 5 months ago
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
Created
2022-02-08
43 commits to main branch, last one 11 months ago
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...
Created
2023-12-06
11 commits to main branch, last one 11 months ago
Machine learning based causal inference/uplift in Python
Created
2019-11-20
304 commits to main branch, last one 12 months ago
This repository has no description...
Created
2021-01-04
636 commits to master branch, last one about a year ago