Dynamic pre and post periods

[DeFilippis]

First, thanks so much for the incredible package! This is an amazing contribution. Second, I'm wondering how to accommodate datasets where the pre- and post- date vary depending on some other variable. For example, let's say I'm trying to evaluate the impact of some policy, but different states implement the policy at different periods of time, and therefore have different "pre" and "post" windows.

I'm guessing this requires a custom model, but the documentation isn't clear on how to accomplish this.

Any thoughts?

[DeFilippis]

Follow-up question: In this paradigm, is there any conception of "clustering" that can be accounted for. e.g. if policies are implemented at the city level at different periods of time, and I'd like to account for correlation in the error term within city. In a standard intervention analysis, you can just cluster standard errors at the level of the intervention. Is there a way to go about doing that in this framework?

For the time being, I've just aggregated the DV across cities to the date level, using a weighted mean of the cities population, which seems sloppy.