How does correlation differ from causation in data science? Can a lack of correlation rule out a causal relationship?
I often ponder the distinction between correlation and causation. While we understand that correlation doesn’t imply causation, does it also mean that if two variables are causally related, there must be some statistical correlation between them? Can causation exist without any detectable correlation?
Although I’m not deeply versed in the philosophical perspectives on causation, my intuition suggests that a lack of correlation could rule out causation. This idea could be valuable for eliminating theories and moving closer to the truth. Is this perspective flawed, or does it offer a valid approach for understanding causation?
No, zero correlation does not mean there is no causation.
Consider driving on a hilly road at a constant speed of 40 mph. On the uphill parts, you press the accelerator harder to maintain that speed, while on the downhill sections, you might not press the accelerator at all, possibly even braking, to keep the speed steady.
In this scenario, there is no correlation between the pressure on the accelerator and the speed because the speed remains constant. Nevertheless, pressing the accelerator is still what causes the car to accelerate.
This is a clear and straightforward example of how a third confounding variable (like the incline in this case) can obscure a correlation. I’ll definitely keep this example in mind.
This complexity is one reason why medicine can be so challenging. The human body is incredibly intricate, and while we rely on heuristics, we don’t fully grasp the underlying systems, yet we still attempt to draw significant conclusions.