Causal Inference - Double Machine Learning

Description

This project explores the concept of the causal inference using observed data, discuss the limitations of Linear Regression, and demonstrate how Double ML can provide more accurate and robust causal estimates. Through a practical example, we will illustrate the steps involved in implementing Double ML and interpreting its results.

Features

You will learn about

• Limitations of controlled Linear Regression to measure causal effects
• Simple intuitive explanation of Frisch-Waugh-Lovell theorem
• Advantages of Double ML
• Potential use cases for implementing Double ML

Getting Started

Prerequisites

• Basic knowledge of Causal Inference - Ladder of causation, causal diagrams, do-calculus, etc.
• Basic knowledge of Linear Regression and Propensity score matching

Implementation

• Execute DoubleML.ipynb to learn from a practical example

Medium article

You can learn more on this topic from my article here