# Lecture 26 Causal Inference Midterm Review

## 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

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

## Causal Diagrams

Identifying X -> Y by closing back doors:

1. Find all the paths from X to Y on the diagram
3. Determine which are already closed by colliders (X -> C <- Y)
4. 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 Supply of housing built, characteristics of the location that lead to BCRs being passed, Demand for housing in the area, the overall economy…