Lecture 20 Untreated Groups
Nick Huntington-Klein
March 12, 2019
Recap
- Last time we discussed fixed effects, a prominent real-world causal inference method that sees a lot of use
- Fixed effects is one way of closing many back doors at once by basically comparing individuals to themselves, across time
- This is important given that, in a social science context, the idea that we can really measure and control for everything in a back door is often implausible
- However, in many contexts, comparing people to themselves is not possible or desirable
Today
- Today we’ll be starting to talk about what are called policy evaluation methods
- Where we have some treatment that has been applied to some people and not others
- (or sometimes, more to some people and less to others)
- And our goal is to figure out how we can compare the treated and the untreated in a way that makes sense
- i.e., apples to apples
The Basic Problem
- It’s common in the “treatment effects” world to refer to the treatment variable as
D
, which is binary (0 or 1)
- Weird, I guess, but no reason not to stick with it. We want to identify
D -> Y
in this (simplified) diagram: