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Create custom data backend/classes for model-submodules.
The base data backend should include $Y$, $D$, $X$ and instruments $Z$.
Then, e.g. t is only added to the DID data Backend, s only for sample-selection models, etc.
n-obs needed for variance estimation (for panel data, n_ids = total observations, scaled by sqrt n * t). May need different n_obs for different asymptotics.
If the Base Data class has implemented the cluster options, how should the workflow, whether ClusterData or not, be handled? By flag indicators or by dynamic properties of the dataframe (cluster_id = None or similar?)
Describe the feature you want to propose or implement
Clusters should be element of the basic DoubleMLData class.
Propose a possible solution or implementation
No response
Did you consider alternatives to the proposed solution. If yes, please describe
No response
Comments, context or references
No response
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