The article emphasizes the importance of establishing causality from observational data, asserting that understanding the narrative behind the data is crucial. It introduces concepts such as Simpson's and Berkson's Paradoxes, demonstrating how misinterpretations can arise when ignoring causal reasoning. The use of causal graphs is recommended to visualize the relations within data better, empowering analysts to delve deeper than mere correlation. The article encourages readers to ask 'Why?' to foster better insights and offers resources to evolve their data analysis toolkit towards encompassing causal inference methods.
Causality may be possible by understanding that the story behind the data is as important as the data itself.
By adding causal graphs to your analysis methods, you can visualize relationships and improve interpretations beyond mere correlation.
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