Did you know there is a relationship between the size of firetrucks and the amount of damage down to a flat during a fire? The bigger the truck sent to put out the fire, the bigger the damages tend to be. The solution is simple: just send smaller firetrucks!
Wait, that doesn’t sound right, does it? Our brain is a huge causal machine, so it can instinctively feel it’s not credible that size of truck and amount of damage done are causally related: there must be another variable explaining the correlation. Here, it’s of course the seriousness of the fire — even better, it’s the common cause of the two correlated variables.
Your brain does that automatically, but what about your computer? How do you make sure it doesn’t just happily (and mistakenly) report the correlation? That’s when causal inference and machine learning enter the stage, as Robert Osazuwa Ness will tell us.
Robert has a PhD in statistics from Purdue University. He currently works as a Research Scientist at Microsoft Research and a founder of altdeep.ai, which teaches live cohort-based courses on advanced topics in applied modeling.
As you’ll hear, his research focuses on the intersection of causal and probabilistic machine learning. Maybe that’s why I invited him on the show… Well, who knows, causal inference is very hard!
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