Cannot obtain inference for DMLCateEstimator

Hi,

I was using DMLCateEstimator (X=None, assuming homogeneous treatment effect), but couldn't obtain the inference for the estimator with const_marginal_effect_inference(). Could anyone help me figure out the problem? Thanks a lot!

1. When I set n_splits as default
est1.const_marginal_effect() and const_marginal_effect_interval() work well; but const_marginal_effect_inference() incurred an error.

est1 = DMLCateEstimator(model_y=GradientBoostingRegressor(n_estimators=500), #n_estimators=30, min_samples_leaf=30
                     model_t=GradientBoostingRegressor(n_estimators=500), #n_estimators=30, min_samples_leaf=30
                     model_final=ElasticNetCV(max_iter=500))
est1.fit(Y=Y, T=T1, W=X1, inference='bootstrap') 
print(est1.const_marginal_effect())
print(est1.const_marginal_effect_interval())
est1.const_marginal_effect_inference()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-29-b224b377fa84> in <module>
----> 1 est1.const_marginal_effect_inference()

~/anaconda3/lib/python3.7/site-packages/econml/_ortho_learner.py in const_marginal_effect_inference(self, X)
    593     def const_marginal_effect_inference(self, X=None):
    594         self._check_fitted_dims(X)
--> 595         return super().const_marginal_effect_inference(X)
    596     const_marginal_effect_inference.__doc__ = LinearCateEstimator.const_marginal_effect_inference.__doc__
    597 

~/anaconda3/lib/python3.7/site-packages/econml/cate_estimator.py in call(self, *args, **kwargs)
    168             name = m.__name__
    169             if self._inference is not None:
--> 170                 return getattr(self._inference, name)(*args, **kwargs)
    171             else:
    172                 raise AttributeError("Can't call '%s' because 'inference' is None" % name)

~/anaconda3/lib/python3.7/site-packages/econml/inference.py in __getattr__(self, name)
     61             raise AttributeError()
     62 
---> 63         m = getattr(self._est, name)
     64 
     65         def wrapped(*args, alpha=0.1, **kwargs):

~/anaconda3/lib/python3.7/site-packages/econml/bootstrap.py in __getattr__(self, name)
    139                 return get_mean()
    140 
--> 141         raise (caught if caught else AttributeError(name))

AttributeError: const_marginal_effect_inference

2. When I customized my own splitter:
n_splits=[(np.arange(train_count),np.arange(train_count, train_count+val_count))]
Similar error happens when I ran const_marginal_effect_inference()
What's worse, the point estimation by est1.const_marginal_effect() is not the mid-point of const_marginal_effect_interval()

3. When I customize my own splitter:
n_splits=StratifiedKFold(n_splits=10).split(X=np.zeros(len(df['stratifier']), y=np.asarray(df['stratifier']))
The est1.fit() method ran but report the error: can't pickle generator objects

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-45cc22f76006> in <module>
      8                      discrete_treatment=False, #default
      9                      random_state=None) #default
---> 10 est1.fit(Y=Y, T=T1, W=X1, inference='bootstrap')
     11 #n_splits=StratifiedKFold(n_splits=10).split(X=np.zeros(train_count+val_count), y=np.asarray(df['zipcode']))
     12 #n_splits=[(np.arange(train_count),np.arange(train_count, train_count+val_count))]

~/anaconda3/lib/python3.7/site-packages/econml/_rlearner.py in fit(self, Y, T, X, W, sample_weight, sample_var, inference)
    294         """
    295         # Replacing fit from _OrthoLearner, to enforce Z=None and improve the docstring
--> 296         return super().fit(Y, T, X=X, W=W, sample_weight=sample_weight, sample_var=sample_var, inference=inference)
    297 
    298     def score(self, Y, T, X=None, W=None):

~/anaconda3/lib/python3.7/site-packages/econml/cate_estimator.py in call(self, Y, T, inference, *args, **kwargs)
     90             if inference is not None:
     91                 # NOTE: we call inference fit *after* calling the main fit method
---> 92                 inference.fit(self, Y, T, *args, **kwargs)
     93             self._inference = inference
     94             return self

~/anaconda3/lib/python3.7/site-packages/econml/inference.py in fit(self, estimator, *args, **kwargs)
     53 
     54     def fit(self, estimator, *args, **kwargs):
---> 55         est = BootstrapEstimator(estimator, self._n_bootstrap_samples, self._n_jobs, compute_means=False)
     56         est.fit(*args, **kwargs)
     57         self._est = est

~/anaconda3/lib/python3.7/site-packages/econml/bootstrap.py in __init__(self, wrapped, n_bootstrap_samples, n_jobs, compute_means, prefer_wrapped)
     48 
     49     def __init__(self, wrapped, n_bootstrap_samples=1000, n_jobs=None, compute_means=True, prefer_wrapped=False):
---> 50         self._instances = [clone(wrapped, safe=False) for _ in range(n_bootstrap_samples)]
     51         self._n_bootstrap_samples = n_bootstrap_samples
     52         self._n_jobs = n_jobs

~/anaconda3/lib/python3.7/site-packages/econml/bootstrap.py in <listcomp>(.0)
     48 
     49     def __init__(self, wrapped, n_bootstrap_samples=1000, n_jobs=None, compute_means=True, prefer_wrapped=False):
---> 50         self._instances = [clone(wrapped, safe=False) for _ in range(n_bootstrap_samples)]
     51         self._n_bootstrap_samples = n_bootstrap_samples
     52         self._n_jobs = n_jobs

~/anaconda3/lib/python3.7/site-packages/sklearn/base.py in clone(estimator, safe)
     53     elif not hasattr(estimator, 'get_params') or isinstance(estimator, type):
     54         if not safe:
---> 55             return copy.deepcopy(estimator)
     56         else:
     57             raise TypeError("Cannot clone object '%s' (type %s): "

~/anaconda3/lib/python3.7/copy.py in deepcopy(x, memo, _nil)
    178                     y = x
    179                 else:
--> 180                     y = _reconstruct(x, memo, *rv)
    181 
    182     # If is its own copy, don't memoize.

~/anaconda3/lib/python3.7/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    278     if state is not None:
    279         if deep:
--> 280             state = deepcopy(state, memo)
    281         if hasattr(y, '__setstate__'):
    282             y.__setstate__(state)

~/anaconda3/lib/python3.7/copy.py in deepcopy(x, memo, _nil)
    148     copier = _deepcopy_dispatch.get(cls)
    149     if copier:
--> 150         y = copier(x, memo)
    151     else:
    152         try:

~/anaconda3/lib/python3.7/copy.py in _deepcopy_dict(x, memo, deepcopy)
    238     memo[id(x)] = y
    239     for key, value in x.items():
--> 240         y[deepcopy(key, memo)] = deepcopy(value, memo)
    241     return y
    242 d[dict] = _deepcopy_dict

~/anaconda3/lib/python3.7/copy.py in deepcopy(x, memo, _nil)
    167                     reductor = getattr(x, "__reduce_ex__", None)
    168                     if reductor:
--> 169                         rv = reductor(4)
    170                     else:
    171                         reductor = getattr(x, "__reduce__", None)

TypeError: can't pickle generator objects

The est1.const_marginal_effect() method works, but est1.const_marginal_effect_interval() and est1.const_marginal_effect_inference() do not work,

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-17-9816c0c44639> in <module>
      6 
      7 #shape(obs, 2): obtain 95% CI of marginal treatment effect for each obs
----> 8 print(np.shape(est1.const_marginal_effect_interval(alpha=.05)))
      9 print(est1.const_marginal_effect_interval(alpha=.05))
     10 

~/anaconda3/lib/python3.7/site-packages/econml/_ortho_learner.py in const_marginal_effect_interval(self, X, alpha)
    588     def const_marginal_effect_interval(self, X=None, *, alpha=0.1):
    589         self._check_fitted_dims(X)
--> 590         return super().const_marginal_effect_interval(X, alpha=alpha)
    591     const_marginal_effect_interval.__doc__ = LinearCateEstimator.const_marginal_effect_interval.__doc__
    592 

~/anaconda3/lib/python3.7/site-packages/econml/cate_estimator.py in call(self, *args, **kwargs)
    167         def call(self, *args, **kwargs):
    168             name = m.__name__
--> 169             if self._inference is not None:
    170                 return getattr(self._inference, name)(*args, **kwargs)
    171             else:

AttributeError: 'DMLCateEstimator' object has no attribute '_inference'

Reproduce the reported failures from DMLCateEstimator using the default and custom splitters, then trace the inference calls through econml/inference.py, bootstrap.py, _ortho_learner.py, and cate_estimator.py. Check how splitter values are cloned and how inference methods are exposed. Done means inference and interval calls work for supported splitters and generator-based splitters no longer fail during fit.