Fast Causal Inference is Tencent's first open-source causal inference project. It is an OLAP-based high-performance causal inference (statistical model) computing library, which solves the performance bottleneck of existing statistical model libraries (R/Python) under big data, and provides causal inference capabilities for massive data execution in seconds and sub-seconds. At the same time, the threshold for using statistical models is lowered through the SQL language, making it easy to use in production environments. At present, it has supported the causal analysis of WeChat-Search, WeChat-Video-Account and other businesses, greatly improving the work efficiency of data scientists.
Basic causal inference tools
Advanced causal inference tools
Already supported multiple businesses within WeChat, such as WeChat-Video-Account, WeChat-Search, etc.
github: https://github.com/Tencent/fast-causal-inference
The machine needs to install and start the docker service
Linux:
Centos:
yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo
yum install docker-ce
systemctl start docker
Ubuntu:
sudo apt-get install docker-ce
verify docker service status:
systemctl status docker
Install docker-compose container service orchestration tool
pip3 install --upgrade pip && pip3 install docker-compose
MacOS:
reference to https://docs.docker.com/desktop/install/mac-install/, Directly download the .dmg package and double-click to install it,
Please make sure the docker service is running
Add PATH:
echo 'export PATH="/Applications/Docker.app/Contents/Resources/bin:$PATH"' >> ~/.bash_profile && . ~/.bash_profile
verify docker service status:
docker ps
git clone https://github.com/Tencent/fast-causal-inference
cd fast-causal-inference && sh bin/deploy.sh
http://127.0.0.1To start causal analysis, please refer to the built-in demo.ipynb