What are the challenges in proving causation with observational data?
Understanding the nuances of observational data is crucial for data scientists. Unlike experimental data, where you can control variables and directly test hypotheses, observational data simply records what happens in the natural course of events. This makes establishing causation—a cause-and-effect relationship—rather tricky. You might observe that two variables move together, but it's a leap to conclude that one causes the other. There could be hidden factors at play, or it might be a case of correlation rather than causation. As you delve into the world of data science, grappling with these challenges is key to drawing accurate conclusions from your data.
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Amey TalekarAnalytics @ Uber | Building Bridges Between Data & Insights | Ex-LTIMindtree
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Lum Kastrati, MDMedical Doctor| Visiting Researcher - Stanford University| PhD candidate-Epidemiology & Biostatistics|
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Ian HowardHealthcare Data Science | Real World Evidence | Real World Data | Machine Learning | Clinical Epidemiology | Clinical…