Causal Inference and Discovery in Python

The field of Data Science, although seemingly complex, is in essence an exploration of information and the patterns within.

Data science encompasses various methods and theories drawn from many fields within the context of mathematics, statistics, and computer science. This repository is devoted to a deeper understanding of Causal Inference in python, particularly for Data Science and Machine Learning, with exercises and solutions from the well-regarded book "Causal Inference & Dicovery in Python" (Aleksander Molak, 2023).

The primary aim of this repository is to provide an accessible yet comprehensive resource to those embarking on their journey in the field of data science. The repository will provide explanations, solutions, and insights into the numerous exercises contained within the aforementioned book.

Content Review:

Section 1: (Causality: Jude Pearl & Ladder of causation, Regression:Observation & Interventions, Graphical Models)

Section 2: (Causal Inference: Nodes-edges & Statistical (In)dependence, The Four-step process, Causal models, inference & M.L)

Section 3: (Causal Discovery: Causal Graph, Discovery & M.L, Assumptions & applications)

Usage and Contribution

This repository is open for all to use and learn from. However, keep in mind that this repository is meant to be a supplement to your learning and not a substitute for the book itself.

If you wish to contribute to this repository, please feel free to open a pull request. Let's cultivate a collaborative space where knowledge can be shared and gained.

Note:

As this repository strictly serves an educational purpose, it abides by the guidelines set forth regarding fair use. It does not aim to infringe upon any copyrights held by the author or the publisher.

Citation:

Aleksander Molak. (2023). Causal Inference & Discovery in Python. Packt ISBN 978-1-80461-298-9

Remember: "It's for the ultimate end of science."

Disclaimer

This repository is unofficial and not affiliated, endorsed or certified by Aleksander Molak - Causal Inference in Python. It has been created for educational purposes, and the repository owner is not responsible for any incorrect information or misuse.

Your journey in the exciting world of data science begins here. Dive in, explore, and let's learn together.

Virtual Environment

pip install --upgrade pip
python3 -m pip install virtualenv
python3 -m venv env
source env/bin/activate
source env/bin/deactivate
pip3 install -r requirements.txt

Github Environment

Performed from Terminal Console

1. git init
2. git remote add origin ["copy here ssh or https"]
3. git remote -v
4. git add -A
5. git add .
6. git commit -m "insert here your commit"
7. git status
8. git push origin master

Additional GitHub Commands

if you already created your repository, then:

1. git remote add origin ["copy here ssh or https"] 
2. same procedure applied above
3. Note: if you already got your ReadMe.md & License.md then,
firstly request your git pull origin master. THIS IS ALWAYS A RECOMMENDED PRACTICE.
4. git push origin master