Scientific Researchīasic research is deeply interested in mechanisms and root causes. As data science, statistics, machine learning, and AI increase their impact on business, it’s all the more important to re-evaluate techniques for establishing causality. It’s not an accident that most heuristics about causality are negative-it’s easier to disprove causality than to prove it. We have heuristics around when causality may not exist, such as “correlation doesn’t imply causation” and “past performance is no indication of future returns,” but pinning down causal effects rigorously is challenging. Causal inference also enables us to design interventions: if you understand why a customer is making certain decisions, such as churning, their reason for doing so will seriously impact the success of your intervention.
And when used badly, we merely repeat the same mistakes we’ve already made. When used correctly, data allows us to infer something about the future based on what happened in the past. If we lower prices, will sales increase? ( The answer is sometimes no.) If we impose a fine on parents who are late picking up their children from daycare, will lateness decrease? ( No, lateness is likely to increase.) Causality is essential in medicine: will this new drug reduce the size of cancer tumors? (That’s why we have medical trials.) This kind of reasoning involves imagination: we need to be able to imagine what will happen if we do X, as well as if we don’t do X. It’s essential to business decisions, and often elusive. Learn moreĬausality allows us to reason about the world and plays an integral role in all forms of decision making.
#WHAT IS CAUSALITY IN RESEARCH TRIAL#
Get a free trial today and find answers on the fly, or master something new and useful. Join the O'Reilly online learning platform. In an article in MIT Technology Review, Jeannette Wing says that “Causality…is the next frontier of AI and machine learning.” Human consciousness may be a stretch, but causation is about to cause a revolution in how we use data. We’re imagining what the world would be like under different sets of circumstances: what would happen if we do X? What would happen if we do Y instead? Judea Pearl, in The Book of Why, goes so far as to say that reaching the top of the “ladder of causation” is “a key moment in the evolution of human consciousness” (p. Whenever we consider the potential downstream effects of our decisions, whether consciously or otherwise, we are thinking about cause. (If I stroke my lucky rabbit’s foot before playing the slot machine, will I hit a jackpot?) If I shoot the cue ball at this angle, will the 3 ball go into the corner pocket? What would happen if I tried a different angle? If I get vaccinated, am I more or less likely to get COVID? We make decisions like these all the time, both good and bad. Whether we are shooting pool or getting vaccinated, we are always thinking about causality.
The Unreasonable Importance of Causal Reasoning
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