User-provided propensity scores?

[BarkleyBG]

A user may want to provide propensity scores (estimated by a different method, say). Two options I've thought of for this:

1. model_method='user' and user specifies weights as an additional argument

   • In this case, the user can be asked to specify a placeholder in the formula, like outcome | exposure ~ anything | group. The propensity_formula=exposure~anything will never be called.
   • The weights argument would get passed into the appropriate functions and naive variance estimation would take place.
   • A downside would be that the weights may need to be in correct form. Of course, a helper function could easily be written from wght_matrix()

2. model_method='user' and user includes the weights as a term in the data argument, and then specifies this with the formula method.

   • This may be a little cleaner, because the user would need only to provide formula= outcome | exposure ~ prop_score | group where prop_score is the column name in data that contains the relevant PS or IPW information.
   • A downside to this approach is the cluster propensity scores are invariant for all individuals in the group, so this is a slight copying of information
   • An upside of this approach is it may be easier to implement with Hajek-style IPW (future work), as you could specify exposure ~ group_prop_score + individual_conditional_prob

I guess I'm leaning towards approach 2 here. Does that sound good to you?

[bsaul]

I don’t have strong feelings on this either way.

…

On May 11, 2017, at 4:12 PM, Brian G. Barkley ***@***.***> wrote: A user may want to provide propensity scores (estimated by a different method, say). Two options I've thought of for this: model_method='user' and user specifies weights as an additional argument In this case, the user can be asked to specify a placeholder in the formula, like outcome | exposure ~ anything | group. The propensity_formula=exposure~anything will never be called. The weights argument would get passed into the appropriate functions and naive variance estimation would take place. A downside would be that the weights may need to be in correct form. Of course, a helper function could easily be written from wght_matrix() model_method='user' and user includes the weights as a term in the data argument, and then specifies this with the formula method. This may be a little cleaner, because the user would need only to provide formula= outcome | exposure ~ prop_score | group where prop_score is the column name in data that contains the relevant PS or IPW information. A downside to this approach is the cluster propensity scores are invariant for all individuals in the group, so this is a slight copying of information An upside of this approach is it may be easier to implement with Hajek-style IPW (future work), as you could specify exposure ~ group_prop_score + individual_conditional_prob I guess I'm leaning towards approach 2 here. Does that sound good to you? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#19>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AEqCLj3kdU9AziCHwvF45bXpjFUT0Oi4ks5r42u8gaJpZM4NYftU>.

[BarkleyBG]

sounds good. Barring any issues, I'll use method 2 and require that user specifies exposure~ cps for cluster propensity score.