# Lecture 16 Back Doors

## Recap

• We’ve now covered how to create causal diagrams
• (aka Directed Acyclic Graphs, if you’re curious what “dag”itty means)
• We simply write out the list of the important variables, and draw causal arrows indicating what causes what
• This allows us to figure out what we need to do to identify our effect of interest

## Today

• But HOW? How does it know?
• Today we’ll be covering the process that lets you figure out whether you can identify your effect of interest, and how
• It turns out, once we have our diagram, to be pretty straightforward
• So easy a computer can do it!

## The Back Door and the Front Door

• When you do data analysis, it’s like observing that someone left their house for the day
• When you do causal inference, it’s like asking how they left their house
• You want to make sure that they came out the front door, and not out the back door, not out the window, not out the chimney

## The Back Door and the Front Door

• Let’s go back to this example

## The Back Door and the Front Door

• We’re interested in the effect of IP spend on profits. That means that our front door is the ways in which IP spend causally affects profits
• Our back door is any other thing that might drive a correlation between the two - the way that tech affects both

## Paths

• In order to formalize this a little more, we need to think about the various paths
• We observe that you got out of your house, but we want to know the paths you might have walked to get there
• So, what are the paths we can walk to get from IP.spend to profits?

## Paths

• We can go `Ip.spend -> profit`
• Or `IP.spend <- tech -> profit`