I'm pondering a scenario involving some insurance data but this could be relevant in many fields. The idea is that I have a total count of some event. Let's imagine this count is the # of attorney negotiations present in a cohort of insurance claims. Lets say I have 1,000 of them in my data.
If I hypothesize that 10% of these attorney negotiations end up going to court (litigation) I want to estimate the additional amount of money spent on litigation.
What type of models or methods could I use in this scenario? I know regression is plausible but I want to be cautious of extrapolating outside of my predictor values (in this case litigation %) in the case that I want to create some extreme what-if scenarios for discussion.
It seems simple in nature but I'd like to have reasonably accurate estimates. Maybe some sort of simulation is in order? Or is an A-B Test applicable here ?
Concrete example with numbers:
Total # of Attorney Negotations: 1000 Actual data Litigation % is around 5%. Hypothesize the additional amount spent on litigation if Litigation is 1% or 10%.
Any suggestions would be welcome.