How to make predictions with models_y?
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Description
I have the following setup (no W variables):
est = LinearDML(model_y=LinearRegression(), model_t=LogisticRegression(max_iter=1000), discrete_treatment=True, random_state=55)
est.fit(Y=y, T=T, X=X, W=None)
I have 8505 observations, and 78 features in X. I am able to make predictions using models_t: est.models_t[0][0].predict(X) but I'm not able to do with with models_y. I get "ValueError: X has 78 features per sample; expecting 6162".
Why does model_y not expect 78 features? What is the correct way to format input to make predictions using models_y?
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Research direction
Start with the LinearDML setup in the issue and read the model_y and model_t documentation or source to determine their expected prediction inputs. Document the correct input format for models_y, explain why it differs from models_t, and include a working prediction example based on the shown estimator.
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Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 32/100