# Trending repositories for topic
**causal-inference**

Next generation of automated data exploratory analysis and visualization platform.

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

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 ...

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.

CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

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

Next generation of automated data exploratory analysis and visualization platform.

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.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

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 ...

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 ...

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.

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 curated list of causal inference libraries, resources, and applications.

An index of algorithms for learning causality with data

CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Notebooks for Applied Causal Inference Powered by ML and AI

:exclamation: uplift modeling in scikit-learn style in python :snake:

A Python package for causal inference in quasi-experimental settings

Notebooks for Applied Causal Inference Powered by ML and AI

A curated list of causal inference libraries, resources, and applications.

python implementation of Peng Ding's "First Course in Causal Inference"

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

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.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

:exclamation: uplift modeling in scikit-learn style in python :snake:

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

A Python package for causal inference in quasi-experimental settings

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.

A curated list of causal inference libraries, resources, and applications.

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Notebooks for Applied Causal Inference Powered by ML and AI

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.

An index of algorithms for learning causality with data

python implementation of Peng Ding's "First Course in Causal Inference"

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

:exclamation: uplift modeling in scikit-learn style in python :snake:

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

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 causal inference libraries, resources, and applications.

Scikit-learn compatible decision trees beyond those offered in scikit-learn

A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

A Python package for causal inference in quasi-experimental settings

An index of causal inference based recommendation algorithms (TOIS).

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

python implementation of Peng Ding's "First Course in Causal Inference"

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.

A curated list of causal inference libraries, resources, and applications.

A Python package for causal inference in quasi-experimental settings

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

An index of algorithms for learning causality with data

A Python library that helps data scientists to infer causation rather than observing correlation.

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

python implementation of Peng Ding's "First Course in Causal Inference"

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

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...

Notebooks for Applied Causal Inference Powered by ML and AI

A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more

A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.

Active Bayesian Causal Inference (Neurips'22)

A curated list of causal inference libraries, resources, and applications.

🛠 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

Scikit-learn compatible decision trees beyond those offered in scikit-learn

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).