Cause and effect weave a tangled web, but a new data analysis tool called MIC can help make sense of it all. The Weizmann Institute writes that “Large data sets with thousands of variables are increasingly common in fields as diverse as genomics, physics, political science, economics and more.” Evaluating pairs of variables from among the thousands, MIC assigns each a score based on the strength of the relation between its variables. For example, while combing through an incredibly complex dataset from the World Health Organization, MIC observed that “obesity increases monotonically with income in the Pacific Islands,” where girth is a sign of status. MIC could also be used to build baseball rosters, Moneyball style. Meanwhile, on Denialism Blog, Mark Hoofnagle explains why healthcare is so expensive in the United States. Hospitals must treat all comers, so the uninsured are more likely to enter the most expensive point of care (the emergency room), at the most advanced stage of illness or injury, and end up incurring the highest bill. Hoofnagle writes, “As a result, to pay for excessive care of the uninsured, all procedures, all tests, all imaging, and all hospitalizations cost more.” Furthermore, healthcare providers are encouraged to order more tests and perform more procedures in order to maximize revenue and hedge their bets.