We talked in class that using language of conditional expectation and regression can help identify issues with causal statements or analysis. In this worksheet, we will go through that concept step by step in case you need more practice with it.
Example 1: How to help people with delinquent hospital bills
This example is drawn from a true story. At a university hospital in Virginia a hospital manager gather a team of workers (MDs, nurses, etc) to improve on the policy of the hospital. One main issue that they needed to tackle is the many patients that had delinquent hospital bills. Delinquent is a financial terms that means a bill is 90 days past due. This team was able to get lots of information about their patients and perform some analysis. Here is a summary of that exercise: “We started collecting information about people who have large unpaid medical bills, and we discover that among these individuals, the main common characteristics is that they are past due on their electrical bill. Hence we are creating a hospital policy to cover their electrical bill for the time they are in the hospital”
- What’s the comparison they are doing using conditional expectation?
- What is the causal effect they are trying to identify, and what would be the regression version of that causal statement?
- Think about the differences between those two. What do you notice?
- What should they have done?
Example 2: Drinking and Birthweight
What’s the exercise they are doing in conditional expectation?
What’s the causal claim they are making expressed as a regression?
In this example this tracks well, the causal framework and the conditional expectation match, therefore the at least the comparison is sensible. The second step here would be to wonder, what are reasons that we may find an effect like that, while drinking having no effect on babies birthweight. This would give you a reason of why this comparison, although sound, it’s biased.
Example 3: Interviews to CEO
On your own or with your study partner, use conditional expectation language or regression to unveil if that makes sense or not.