Statistics for topic causal-inference
RepositoryStats tracks 605,145 Github repositories, of these 114 are tagged with the causal-inference topic. The most common primary language for repositories using this topic is Python (55). Other languages include: Jupyter Notebook (22)
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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.
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...
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
A Python package for causal inference in quasi-experimental settings
Active Bayesian Causal Inference (Neurips'22)
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
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 Discovery in Python. It also includes (conditional) independence tests and score functions.
A Python package for causal inference in quasi-experimental settings
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.
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...
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
A curated list of causal inference libraries, resources, and applications.
Notebooks for Applied Causal Inference Powered by ML and AI
Active Bayesian Causal Inference (Neurips'22)
python implementation of Peng Ding's "First Course in Causal Inference"
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
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.
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 ...
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...
A curated list of causal inference libraries, resources, and applications.
Fast and customizable framework for automatic and quick Causal Inference in Python
Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024
Notebooks for Applied Causal Inference Powered by ML and AI
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024
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.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Notebooks for Applied Causal Inference Powered by ML and AI
Fast and customizable framework for automatic and quick Causal Inference in Python
