Causal Effects via Propensity Scores - Introduction and Python Implementation

Explore the concept of causal effects through propensity scores in this 18-minute video tutorial. Dive into the differences between observational and interventional studies before delving into the propensity score concept. Learn about three propensity score-based methods: matching, stratification, and inverse probability of treatment weighting. Follow along with a practical example demonstrating how to calculate the Average Treatment Effect (ATE) of graduation on income using Python code. Gain insights into the application of these methods in real-world scenarios, and understand the importance of cautious interpretation when working with observational data. Access additional resources, including a series playlist, blog post, and example code, to further enhance your understanding of causal inference techniques.

Introduction -
Observational vs Interventional Studies -
Propensity Score -
3 Propensity Score-based Methods -
1 Matching -
2 Stratification -
3 Inverse Probability of Treatment Weighting -
Example: ATE of Grad on Income -
Word of Caution -