Multidimensional treatment with XGBRegressior support
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Description
Is it possible to use CausalForestDML with a multidimensional treatment (5 continuous columns) with XGBRegressor? I see model_y accepts XGBRegressor, but model_t complains in a following way:
def _validate_meta_shape(data):
if hasattr(data, 'shape'):
assert len(data.shape) == 1 or (len(data.shape) == 2 and (data.shape[1] == 0 or data.shape[1] == 1))
during fit method. I'm using causal_model = CausalForestDML(model_t=XGBRegressor(), model_y=XGBRegressor(), random_state=0).
Do you know what raises the issue?
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Research direction
Reproduce the issue with CausalForestDML using XGBRegressor for both model_t and model_y, then trace the failure from fit into _validate_meta_shape. Check how multidimensional treatments are validated and whether the requested combination is supported. Done should be a documented resolution with appropriate regression coverage if support is added.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100