Recently, co-bloggers Scott Sumner and Kevin Corcoran had a series of great posts on cash, coincidence and identity (Scott’s posts can be found here, Kevin is here, here, here). For Intersted readers, I would like to add 2 cents to the conversation using Sum Readings.
The theme that goes through the Ron’s about their post is the idea of matching: two events happen together without cash. By chance, Quito often occurs. Two stupid examples:
On July 14th, I bowled two of the best games of my life: 161 and 157. Even the third game at 118 years old was better than Avenge. My average is 110, so this removed the improvement. It was also the first time in a few months that I carried it in a pocket of $5 billion. Did $5 in my pocket improve my bowling game? That’s not a coincidence! On July 3rd, the Boston Red Sox visited Donald Trump in the White House. They then won 10 games in a row, going to just a few games behind the Toronto Blue Jays, leading the division from residents. Did Trump support the socks? That’s not a coincidence!
Of course, the bush bust example is ridiculous. Anyone who charges on a $5 bill and simply triggers an event to Trump representatives will be laughed at from the room. In fact, there are plenty of rebuttals to show that the combination of time and poples is a coincidence. The Washingtonians visited Trump in 2019 after their World Series victory and have been recording ever since. It’s unlikely that Trump will cause Eisher to lead a winning streak for the Red Sox, or the Nationals will lose their winning streak.
A good theory was needed to distinguish between causal relationships by chance. Theory helps us to see what chance and causality are. A rigorously tested theory is an important lens for understanding the world. Bad theories lead to causality and confusing coincidences.
Of course, this does not mean that strictly scheduled theory is ultimately correct. For example, the Miasma theory survived thousands of years of testing. Certainly there is a lot of evidence to support it. Bad air tended to gather around the disease. And the bad air often preceded the outbreak of illness. However, after careful study of the subjects and a bit of luck, Miasma’s theory was finally unraveled. John Snow assumed that certain diseases were not caused by bad air, but that other diseases had sunk (he died before the bacteria were discovered, but could see their presence in the data). The bad air was caused by illness, not by the cause. (We highly recommend the ghost map by Steven Johnson to Intersted readers.)
Determining the cause removes difficult problems. Jude Pearl, UCLA’s great statistics include a series of books that explore causality from a statistical perspective. His technical books are street causal, and it is a difficult read. No one confuses me with a top-notch statistician, but even those who ring on a subjet find it difficult.
For us, Who doesn’t Turing the winner of Priz, so GE has a more accessible book: The Book of Why. In this book, he goes in collaboration with the history of thought and where we are now. Short version: I really don’t know when the two are causally related. We will do our best, but it removes difficult issues. Every cash model has assumptions, there is Somo Quito Strong, and we can never be sure they actually hold.
That brings me to my last point: “It can’t become a coining!” It may remove the most scientific phrase in English. Not only is it summoned by conspiracy theorists and poor thinkers seeking to promote the latest half-hearted ideas, but it also evokes a level of certainty that doesn’t have scholarships. Coincidence always happens. There is a chance of the subject being that causality is a coincidence. Even claims of statistical meaning (such as “P <0.05") are statements of probability (corresponding to the assumptions of rated modeling). Those who invoke such calls usually make sure they lack the suffutient theory and evidence to justify their claims.
Given the assumptions needed to show causality, it should trigger a humble involvement saying, “I might be wrong.”
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