# Lecture 26 Causal Inference Midterm Review

## Nick Huntington-Klein

### March 28, 2019

## Causal Inference Midterm

- Similar format to the homeworks we’ve been having
- At least one question evaluating a research question and drawing a dagitty graph
- At least one question identifying the right causal inference method to use
- At least one question about the feature(s) of the methods
- At least one question carrying out a method in R

## Causal Inference Midterm

- Covers everything up to IV (obviously, a focus on things since the Programming Midterm, but there is a little programming)
- No internet (except dagitty) or slides available this time
- One 3x5 index card, front and back
- You’ll have the whole class period so don’t be late!

## Causal Diagrams

- Consider all the variables that are likely to be important in the data generating process (this includes variables you can’t observe)
- For simplicity, combine them together or prune the ones least likely to be important
- Consider which variables are likely to affect which other variables and draw arrows from one to the other
- (Bonus: Test some implications of the model to see if you have the right one)

## Causal Diagrams

Identifying `X -> Y`

by closing back doors:

- Find all the paths from
`X`

to `Y`

on the diagram
- Determine which are “front doors” (start with
`X ->`

) and which are “back doors” (start with `X <-`

)
- Determine which are already closed by colliders (
`X -> C <- Y`

)
- Then, identify the effect by finding which variables you need to control for to close all back doors (careful - don’t close the front doors, or open back up paths with colliders!)

## Causal Diagrams

- Let’s draw (and justify) a diagram to get the effect of Building Code Restrictions
`BCR`

, which prevent housing from being built, on `Rent`

- Consider perhaps: the
`Sup`

ply of housing built, characteristics of the `loc`

ation that lead to `BCR`

s being passed, `Dem`

and for housing in the area, the overall economy…

## Causal Diagrams Answer

One answer, with non-BCR `Laws`

, `Labor`

market, `econ`

omy: