This chapter is from the book
This chapter is from the book
This chapter is from the book
13.6 Conclusion
In this chapter, you’ve developed the tools to do causal inference. You’ve learned that machine learning models can be useful to get more general model specifications, and you saw that the better you can predict an outcome using a machine learning model, the better you can remove bias from an observational causal effect estimate.
Observational causal effect estimates should always be used with care. Whenever possible, you should try to do a randomized controlled experiment instead of using the observational estimate. In this example, you should simply use randomized control: flip a coin each day to see whether the sprinkler gets turned on. This re-creates the post-intervention system and lets you measure how much less likely the sidewalk is to be slippery when the sprinkler is turned off versus turned on (or when the system isn’t intervened upon). When you’re trying to estimate the effect of a policy, it’s hard to find a substitute for actually testing the policy through a controlled experiment.
It’s especially useful to be able to think causally when designing machine-learning systems. If you’d simply like to say what outcome is most likely given what normally happens in a system, a standard machine learning algorithm is appropriate. You’re not trying to predict the result of an intervention, and you’re not trying to make a system that is robust to changes in how the system operates. You just want to describe the system’s joint distribution (or expectation values under it).
If you would like to inform policy changes, predict the outcomes of intervention, or make the system robust to changes in variables upstream from it (i.e., external interventions), then you will want a causal machine learning system, where you control for the appropriate variables to measure causal effects.
An especially interesting application area is when you’re estimating the coefficients in a logistic regression. Earlier, you saw that logistic regression coefficients had a particular interpretation in observational data: they describe how much more likely an outcome is per unit increase in some independent variable. If you control for the right variables to get a causal logistic regression estimate (or just do the regression on data generated by control), then you have a new, stronger interpretation: the coefficients tell you how much more likely an outcome is to occur when you intervene to increase the value of an independent variable by one unit. You can use these coefficients to inform policy!