368 - Correlation vs Causation in Python: Understanding the Critical Difference (Part 4/4)

Complete your correlation analysis journey by understanding the crucial distinction between correlation and causation! This final tutorial explores why "correlation does not imply causation" through practical examples and demonstrates how to apply causal thinking to data analysis. Using simulated scenarios and the Palmer Penguins dataset, learn to identify confounding variables, bidirectional causation, and spurious correlations.

Topics covered:
Understanding why correlation doesn't imply causation with real-world examples
Simulating confounding variables (ice cream sales vs drowning deaths)
Identifying bidirectional causation (exercise vs happiness)
Recognizing spurious correlations in time series data
Bradford Hill criteria for assessing causality
Applying causal thinking to biological questions
Distinguishing between exploratory correlation and proof of causation
When domain expertise trumps statistical tests

The code from this video is available here: https://github.com/bnsreenu/python_fo...