Wrapping it up

Congrats on finishing Chapter 9!

We presented a lot of information in this chapter! Let’s summarize!

We started with the basics and introduced the matching estimator. On the way, we defined ATE, ATT, and ATC.

Then, we moved to propensity scores. We learned that propensity score is the probability of being treated, which we compute for each observation. Next, we’ve shown that although it might be tempting to use propensity scores for matching, in reality, it’s a risky idea. We said that propensity scores can shine in other scenarios, and we introduced propensity score weighting, which allows us to construct sub-populations and weight them accordingly in order to deconfound our data (it does not help when we have unobserved confounding).

Next, we started our journey with meta-learners. We said that ATE can sometimes hide important information from us and we defined CATE. This opened the door for us to explore the world of HTEs, where units...