Understanding causal inference is essential in today's data-driven world

Over the next few years, the ability to conduct robust causal analyses will become even more critical as the complexity and volume of data continue to grow.

Public policy, healthcare, economics, and social sciences decisions are increasingly based on empirical evidence.

This comprehensive course delves into essential topics including potential outcomes, social experiments, matching, instrumental variables, regression discontinuity, and differences in differences. Participants gain vital tools to accurately discern cause-and-effect relationships, empowering them to make informed policy-making, research, and strategic planning decisions.

This course's unique emphasis on practical, applicable skills and its integration of the latest research developments make it an indispensable resource for anyone aiming to excel in fields that rely heavily on data analysis and evidence-based decision-making.

We will cover key topics such as:

• Potential outcomes
• Social experiments
• Matching
• Instrumental variables
• Regression discontinuity
•  Differences in Differences

This equips learners with the tools necessary to discern cause-and-effect relationships accurately.

As industries and policymakers rely on data to drive decisions, the importance of mastering causal inference cannot be overstated.

Enhance your decision-making skills with advanced causal inference techniques for data-driven policymaking