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You might also find this recent working paper interesting.

You might also find the research by Imai and Kim (2020) quite interesting.

It should be noted, though, that the pattern of the policy dummy matters in DiD settings. See this post where the pattern of the treatment dummy is addressed in detail. The main issue is the "always treated" jurisdictions. It will be difficult—actually impossible—to demonstrate (visually) common trends without sufficient "pre-event" data. You could circumvent this issue by excluding the "always treated" from your analysis. To Dimitriy's point (see comments), this severely limits your sample, leaving you with approximately 30 states, 8 of which will be treated as some point between 1984-2018. You will also have to look into some finite-sample adjustments to deal with your standard errors.

It should be noted, though, that the pattern of the policy dummy matters in DiD settings. See this post where the pattern of the treatment dummy is addressed in detail. The main issue is the "always treated" jurisdictions. It will be difficult—actually impossible—to demonstrate (visually) common trends without sufficient "pre-event" data. You could circumvent this issue by excluding the "always treated" from your analysis. To Dimitriy's point (see comments), this severely limits your sample, leaving you with approximately 30 states, 8 of which will be treated as some point between 1984–2018. You will also have to look into some finite-sample adjustments to deal with your standard errors.

$$ \text{Outcome}_{st} = \alpha + \gamma_{s} + \lambda_{t} + \delta \text{Prohibition}_{st} + \epsilon_{st}, $$

where you observe a continuous outcome in state $s$ in year $t$. $\gamma_{s}$ and $\lambda_{t}$ denote fixed effects for states and years, respectively. The variable $\text{Prohibition}_{st}$ is your policy (i.e., treatment) dummy.

This is still an interaction model, we just define it in a different way. I am going to simulate some datasets in R to help with your intuition. Let's consider a panel dataset with 3 states (i.e., New York, New Jersey, and Connecticut) observed across 6 years (i.e., 2010-2015). Treatment is at the state level. I live in the Tri-State area so the decision regarding which states to use is completely arbitrary.

$$ \text{Outcome}_{st} = \gamma_{s} + \lambda_{t} + \delta \text{Prohibition}_{st} + \epsilon_{st}, $$

where you observe a continuous outcome in state $s$ in year $t$. The parameters $\gamma_{s}$ and $\lambda_{t}$ denote fixed effects for states and years, respectively. The variable $\text{Prohibition}_{st}$ is your policy (i.e., treatment) dummy.

This is still an interaction model, we just define it in a different way. I am going to simulate some datasets in R to help with your intuition. Let's consider a panel dataset with 3 states (i.e., New York, New Jersey, and Connecticut) observed across 6 years (i.e., 2010–2015). Treatment is at the state level. I live in the Tri-State area so the decision regarding which states to use is completely arbitrary.