The question of the observed outcome input for DynamicDML

Hi,

I have a question related to the input of DynamicDML estimation. The comment suggests the input outcome is one-dimensional array with its length = n_groups * n_periods. n_periods means the length of time series of each entity.

However, when I read the Algorithm 1 (which uses Neyman orthogonal g-estimation) in this reference paper [Lewis2021], the data input only uses the final observed outcome Ym. That is, Y = Ym for each sample. Actually all algorithms in this paper just assume single final outcome Ym (not the whole time series for each entity) as the data input.

I am confused why/how to specify a continuous time-step outcome Yi (i=1, ..., m) for the DynamicDML() input. Moreover, which specific algorithm is implemented in the class DynamicDML?

Thanks,

Review the DynamicDML input comment and Algorithm 1 in the Lewis2021 reference linked from the references page. Clarify whether the API expects the full outcome time series or only the final observed outcome, identify which algorithm DynamicDML implements, and update the relevant documentation so the expected input is explicit.