Simply Statistics A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek

Time varying causality in n=1 experiments with applications to newborn care

We just had our second son about a week ago and I’ve been hanging out at home with him and the rest of my family. It has reminded me of a few things from when we had our first son. First, newborns are tiny and super-duper adorable. Second, daylight savings time means gaining an extra hour of sleep for many people, but for people with young children it is more like (via Reddit):


Third, taking care of a newborn is like performing a series of n=1 experiments where the causal structure of the problem changes every time you perform an experiment.

Suppose, hypothetically, that your newborn has just had something to eat and it is 2am in the morning (again, just hypothetically). You are hoping he’ll go back down to sleep so you can catch some shut-eye yourself. But your baby just can’t sleep and seems uncomfortable. Here are a partial list of causes for this: (1) dirty diaper, (2) needs to burp, (3) still hungry, (4) not tired, (5) over tired, (6) has gas, (7) just chillin. So you start going down the list and trying to address each of the potential causes of late-night sleeplessness: (1) check diaper, (2) try burping, (3) feed him again, etc. etc. Then, miraculously, one works and the little guy falls asleep.

It is interesting how the natural human reaction  to this is to reorder the potential causes of sleeplessness and start with the thing that worked next time. Then often get frustrated when the same thing doesn’t work the next time. You can’t help it, you did an experiment, you have some data, you want to use it. But the reality is that the next time may have nothing to do with the first.

I’m in the process of collecting some very poorly annotated data collected exclusively at night if anyone wants to write a dissertation on this problem.