Statistics for topic causal-inference
RepositoryStats tracks 518,991 Github repositories, of these 98 are tagged with the causal-inference topic. The most common primary language for repositories using this topic is Python (48). Other languages include: Jupyter Notebook (19)
Stargazers over time for topic causal-inference
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Fast and customizable framework for automatic and quick Causal Inference in Python
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 ...
A curated list of causal inference libraries, resources, and applications.
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
Next generation of automated data exploratory analysis and visualization platform.
Fast and customizable framework for automatic and quick Causal Inference in Python
A curated list of causal inference libraries, resources, and applications.
Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。英文版原作者：Matheus Facure
A curated list of causal inference libraries, resources, and applications.
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 ...
Fast and customizable framework for automatic and quick Causal Inference in Python
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Fast and customizable framework for automatic and quick Causal Inference in Python
A curated list of causal inference libraries, resources, and applications.
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 ...
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
Next generation of automated data exploratory analysis and visualization platform.
Fast and customizable framework for automatic and quick Causal Inference in Python
A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.
A Python package for causal inference in quasi-experimental settings
python implementation of Peng Ding's "First Course in Causal Inference"
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
Fast and customizable framework for automatic and quick Causal Inference in Python
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...
Next generation of automated data exploratory analysis and visualization platform.
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 ...
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
A curated list of causal inference libraries, resources, and applications.
A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
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...
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization