Okay, so I’m not a pro in this field yet but it looks like I have to become one real quick like as I’m going to be writing two papers about them.  But the more I read about, the more I, you guessed it, can relate it to my book(s).  So what are propensity scores and what do they do?  Well, they match subjects in two groups for certain factors so that only the difference is one that we can attribute to the factor we are most interested in.  So like if we want to see if two treatments are different in treating a disease, we use propensity score matching to make sure that the two treatment groups otherwise match on age, race, and so on.  Sure, there may be some people that can’t be matched between groups, so we don’t use them.  Of course, we could impute data so that unmatched data can have something to match with or someone nice can play matchmaker between me and Joe Mange … anyway, I’m digressing again.  But yeah, that’s propensity score matching in a nutshell.  And another example from my trilogy (yeah, you know I’d be getting to this) would be if, say, our hero, FBI agent, Randy Lipinski, wanted to know if he was in a good or bad dimension or he was getting close to capturing bad Anton.  He could figure the probability of catching bad Anton by look at Anton’s marital history, how many children he has, or where he lives.  Although that could be tricky too because sometimes bad Anton is in a good dimension. Or good Anton is in neither a good or bad dimension but in a null space or … well, anyway, you’ll see if you read Book 2, Revised Orders.  But anywhoo, in one of the good dimensions, good Anton lives with his wife and two kids in Indianapolis and what a coincidence that I came across a travel guide (in our break room when I needed a break from propensity score reading of all things) with this on the front cover.

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Now, don’t they look relaxed and happy and full of bliss? Which is how I hope to look like once I get this propensity score thing downpat.

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