Apply clip_bounds to a user-supplied propensity model

[arpitjain099]

compute_propensity_score documents clip_bounds as keeping the score away from 0 and 1 so later steps do not divide by zero, but the bound only applies when the default model is used. PropensityModel.predict clips internally; a user-supplied classifier goes through predict_proba and its output is returned untouched.

On a separable treatment that saturates:

p_user, _ = compute_propensity_score(X=X, treatment=w, p_model=LogisticRegression(C=1e9, max_iter=1000))
p_def, _ = compute_propensity_score(X=X, treatment=w)
p_user.min(), p_user.max()   # 0.0, 1.0
p_def.min(), p_def.max()     # 0.001, 0.999

Feeding the first one back into the library rejects it, which is what the bound was there to prevent:

BaseDRRegressor(learner=LinearRegression()).fit_predict(X=X, treatment=w, y=y, p=p_user)
AssertionError: The values of p should lie within the (0, 1) interval.

The DR-learner divides by both p_filt and 1 - p_filt, and TMLE by p and 1 - p, so the guard is the only thing standing between a saturated propensity model and an infinity.

The returned score is now clipped whichever model produced it. For the built-in models this is a no-op, since predict has already clipped to the same bounds. BaseRLearner(propensity_learner=...) reaches the unclipped path through self.model_p, so it is covered too.

Two tests: a user-supplied LogisticRegression on separable data has to come back inside the default bounds, which fails on master with assert np.float64(0.0) >= 0.001, and custom bounds have to hold as well.

pytest tests/test_propensity.py tests/test_cate_scoring.py is 39 passing, and black is clean on both files.