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How to Tell if Correlation Implies Causation Posted on January 1, 2015by statswithcats You’ve probably heard the admonition: Correlation Does Not Imply Causation. Everyone agrees that correlation is not the same as causation. However, those two words — correlation and causation — have generated quite a bit of discussion. Why Causality Matters No one gets perturbed if you say two conditions or events are correlated but even suggest that causation is possible and you’ll get the clichéd admonition and perhaps with even harsher criticism. It’s not easy to prove causality, though, so there must be a reason for putting in the effort. For example, if you can figure out what causes a condition or event, you can: Promote the relationship to reap benefits, such as between agricultural methods and crop production or pharmaceuticals and recovery from illnesses. Prevent the cause to avoid harmful consequences, such as airline crashes and manufacturing defects. Prepare for unavoidable harmful consequences, such as natural disasters, like floods. Prosecute the perpetrator of the cause, as in law, or lay blame, as in politics. Pontificate about what might happen in the future if the same relationship occurs, such as in economics. Probe for knowledge based on nothing more than curiosity, such as how cats purr. So how can you tell if correlation does in fact imply causation? Criteria for Causality Sometimes it’s next to impossible to convince skeptics of a causal relationship. Sometimes it’s even tough to convince your supporters. Developing criteria for causality has been a topic of concern in medicine for centuries. Several sets of criteria have been proffered over those years, the most widely cited of which are the criteria described in 1965 by Austin Bradford Hill, a British medical statistician. Hill’s criteria for causation specify the minimal conditions necessary to accept the likelihood of a causal relationship between two measures as: Strength: A relationship is more likely to be causal if the correlation coefficient is large and statistically significant. Consistency: A relationship is more likely to be causal if it can be replicated. Specificity: A relationship is more likely to be causal if there is no other likely explanation. Temporality: A relationship is more likely to be causal if the effect always occurs after the cause. Gradient: A relationship is more likely to be causal if a greater exposure to the suspected cause leads to a greater effect. Plausibility: A relationship is more likely to be causal if there is a plausible mechanism between the cause and the effect. Coherence: A relationship is more likely to be causal if it is compatible with related facts and theories. Experiment: A relationship is more likely to be causal if it can be verified experimentally. Analogy: A relationship is more likely to be causal if there are proven relationships between similar causes and effects. These criteria are sound principles for establishing whether some condition or event causes another condition or event. No individual criterion is foolproof, however. That’s why it’s important to meet as many of the criteria as is possible. Still, sometimes causality is unprovable. Three Steps to Decide if Correlation Implies Causation ... https://statswithcats.wordpress.com/2015/01/01/how-to-tell-if-correlation-implies-causation/