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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
A resource list for causality in statistics, data science and physics
causal
physics
causality
causation
statistics
data-science
causal-impact
causal-models
meta-learning
causal-networks
causal-discovery
causal-inference
machine-learning
bayesian-inference
causality-analysis
statistical-physics
causality-algorithms
statistical-inference
statistical-mechanics
causal-machine-learning
Created
2018-01-04
238 commits to master branch, last one 11 hours ago
🛠 How to Apply Causal ML to Real Scene Modeling?How to learn Causal ML?【✔从Causal ML到实际场景的Uplift建模】
Created
2022-11-11
27 commits to master 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
Scikit-learn compatible decision trees beyond those offered in scikit-learn
Created
2022-05-11
374 commits to main branch, last one about a month ago
A General Causal Inference Framework by Encoding Generative Modeling
Created
2022-06-07
238 commits to main branch, last one 5 months ago
🎯 :closed_book: Targeted Learning in R: A Causal Data Science Handbook
Created
2019-03-23
501 commits to master branch, last one 2 months ago
가짜연구소 <인과추론과 실무> 프로젝트
Created
2024-04-28
48 commits to main branch, last one 2 months ago
Taking causal inference to the extreme!
s-learner
t-learner
x-learner
r-learning
metalearner
metalearning
doubly-robust
g-computation
assumption-check
causal-inference
quasi-experimental
nonparametric-model
sensitivity-analysis
observational-studies
causal-machine-learning
double-machine-learning
interrupted-time-series
extreme-learning-machine
debiased-machine-learning
Created
2023-02-12
423 commits to main branch, last one 23 days ago