Inverse probability weighting

So if two people both have a propensity score of point seven five, they have the exact same baseline likelihood of being treated.

And once we have that score, one of the most powerful things we can do with it is inverse propensity score weighting, or IPW.

Let's really drill down into the mechanics of IPW because it's brilliant, but honestly counterintuitive.

It can be.

IPW operates by constructing a pseudo population where the treatment assignment is no longer confounded by the covariates.

It achieves this by weighting each individual in your dataset by the inverse of the probability of receiving the treatment they actually received.

Let me test an analogy here to see if I'm grasping the math.

So in my dataset, I have hundreds of elite athletes who took the boot camp, but I only have one sedentary guy who accidentally wandered in and took it.