Causality

Crash Course in Causality

Overview

This repository contains the Jupyter Notebook "Sonali_Bandi_INFO7390_Crash Course in Causality_Written Section", an educational resource aimed at providing a comprehensive introduction to the field of causality. It is ideal for students, academics, and professionals interested in causal inference and its applications in data science.

Table of Contents

• Introduction
• Notebook Contents
• Getting Started
• Contributing
• License

Introduction

Causality is a fundamental concept in data analysis, crucial for understanding the relationship between variables and making informed decisions based on data. This notebook explores various aspects of causality, from basic principles to advanced applications.

Notebook Contents

1. Introduction to Causality
2. Fundamentals of Causal Inference
3. Statistical Methods in Causality
4. Experimental Design
5. Observational Data and Causal Discovery
6. Case Study: Impact of an Intervention on Achievement Scores in School Settings
   • Dataset Features
   • Effectiveness of the Intervention
   • Exploratory Data Analysis (EDA)

Getting Started

To use this notebook:

1. Clone the repository:

git clone https://github.com/sonalibandi/Causality

2. Install the necessary dependencies: pip install -r requirements.txt

3. Run the Jupyter Notebook: jupyter notebook Sonali_Bandi_INFO7390_Crash_Course_in_Causality_Written_Section.ipynb

Contributing

Contributions to this notebook are welcome. If you have suggestions or improvements, feel free to fork the repository and submit a pull request.

License

This project is licensed under the [MIT License]