An essential guide to understanding how instrumental variables can uncover causal effects in observational studies, but the key to success lies in their proper application.
Theoretical clarity meets practical application in Difference-in-Differences, but understanding its assumptions is key to accurate insights—continue reading to learn more.
With clear examples and simple language, causal inference reveals how scientists determine what truly causes what—and why it matters for understanding the world around us.
