With recent policy changes and an increasing focus on data-driven decision-making, mastering advanced DiD techniques is crucial

In the last few years, we have witnessed an unprecedentedly fast change in our understanding of difference-in-differences (DiD) methods for policy evaluation. Understanding modern developments in DiD is highly relevant today due to the method's widespread application in evaluating public policies, economic interventions, and social programs. 

Over the past decade, DiD has evolved significantly, addressing various complexities in real-world data. 

This course covers foundational concepts, such as:

• The potential outcomes framework
• The parallel trends assumption
•  Staggered DiD
• Non-parametric estimators
• Synthetic control methods. 

You will delve into essential topics including potential outcomes, social experiments, matching, instrumental variables, regression discontinuity, and difference-in-differences.

 Gain vital tools to accurately discern cause-and-effect relationships, and make informed policy-making, research, and strategic planning decisions