Trending repositories for topic causal-inference
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 ...
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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 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.
A Python library that helps data scientists to infer causation rather than observing correlation.
An index of algorithms for learning causality with data
Next generation of automated data exploratory analysis and visualization platform.
A Python package for causal inference in quasi-experimental settings
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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...
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 Python library that helps data scientists to infer causation rather than observing correlation.
An index of algorithms for learning causality with data
Next generation of automated data exploratory analysis and visualization platform.
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...
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 ...
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
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
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
An index of algorithms for learning causality with data
🛠 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】
A Python package for causal inference in quasi-experimental settings
A Python library that helps data scientists to infer causation rather than observing correlation.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
Must-read papers and resources related to causal inference and machine (deep) learning
Next generation of automated data exploratory analysis and visualization platform.
🛠 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】
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
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
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 Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
A Python package for causal inference in quasi-experimental settings
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Must-read papers and resources related to causal inference and machine (deep) learning
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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 ...
An index of algorithms for learning causality with data
A Python library that helps data scientists to infer causation rather than observing correlation.
Next generation of automated data exploratory analysis and visualization platform.
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...
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Next generation of automated data exploratory analysis and visualization platform.
A curated list of causal inference libraries, resources, and applications.
An index of algorithms for learning causality with data
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 Python package for causal inference in quasi-experimental settings
A Python library that helps data scientists to infer causation rather than observing correlation.
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
Tutorial for a target trial emulation with a time-varying exposure, time-dependent confounding, time-to-event outcome, and Sequentially Doubly Robust estimation (Hoffman et al. 2022).
The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean o...
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.
🛠 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】
This Python library implements Trimmed Match for analyzing randomized paired geo experiments and also implements Trimmed Match Design for designing randomized paired geo experiments.
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Notebooks for Applied Causal Inference Powered by ML and AI
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
[KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua.
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.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
:package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
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...
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Next generation of automated data exploratory analysis and visualization platform.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
An index of algorithms for learning causality with data
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
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
A Python package for causal inference in quasi-experimental settings
A Python library that helps data scientists to infer causation rather than observing correlation.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
A Python package for modular causal inference analysis and model evaluations
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
Notebooks for Applied Causal Inference Powered by ML and AI
Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024
The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean o...
Fast and customizable framework for automatic and quick Causal Inference in Python
Python implementation of Richard McElreath's Statistical Rethinking 2023 lecture series on causal inference & Bayesian estimation.
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
python implementation of Peng Ding's "First Course in Causal Inference"
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 awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
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
Tutorial for a target trial emulation with a time-varying exposure, time-dependent confounding, time-to-event outcome, and Sequentially Doubly Robust estimation (Hoffman et al. 2022).
🛠 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】
