Lecture 20 Untreated Groups

Nick Huntington-Klein

March 12, 2019


  • 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 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: