Practice Computing Causal Effects Using Indirect Inference: CreditCo

Now let's repeat the previous CreditCo analysis, but now use indirect inference to compute the causal effect of opting in to a credit limit increase on one's credit balance.

The setting is the same as before. CreditCo sent out offers in the mail to increase their customers' credit limits. But we know that taking up the offer is not randomly determined. We proposed to solve this problem by using the whether it rained on the day the credit offers were delivered as an instrument for takeup.

In this exercise, we will restrict our analysis to the set of customers who received the offer. We will use the method of indirect inference to estimate the causal effect of takeup on credit balance.

Included on the workspace is a data set from CreditCo. The data are a sample of CreditCo customers who were offered the credit limit increase. We will focus on the variable rainy, which we will use as an instrument for opt_in, which we suspect is endogenous to our outcomes of interest.

1) Using a linear regression, compute the reduced-form effect (i.e. correlation between credit balance and rain)
2) Using a linear regression, compute the first-stage effect (i.e. correlation between opting in and rain)
3) Now compute the causal effect, which is equal to (reduced form)/(first stage)
4) Did opting in to the credit offer increase or decrease credit balances?

Exercise

Practice Computing Causal Effects Using Indirect Inference: CreditCo

Instructions