can't use bootstrap inference with xlearner

I'm trying to fit x_learner and get CI. I can fit it without inference without error, but when I put BootstrapInference, it give the following error.
Did I miss anything?

X_learner.fit(df_avg.outcome, (df_avg.assignment=='treatment').astype(int), df_avg[heteX],
inference= BootstrapInference(n_bootstrap_samples=10,n_jobs=-1))

---

Empty Traceback (most recent call last)
~\AppData\Roaming\Python\Python36\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
807 try:
--> 808 tasks = self._ready_batches.get(block=False)
809 except queue.Empty:

~\Anaconda3\lib\queue.py in get(self, block, timeout)
160 if not self._qsize():
--> 161 raise Empty
162 elif timeout is None:

Empty:

During handling of the above exception, another exception occurred:

KeyError Traceback (most recent call last)
in ()
16 df_avg2 = df_avg.dropna()
17 X_learner.fit(df_avg2.booking_value_usd2, (df_avg2.assignment=='treatment').astype(int), df_avg2[heteX],
---> 18 inference='bootstrap')#BootstrapInference(n_bootstrap_samples=10,n_jobs=-1))
19
20

~\AppData\Roaming\Python\Python36\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

~\AppData\Roaming\Python\Python36\site-packages\econml\inference.py in fit(self, estimator, *args, **kwargs)
49 def fit(self, estimator, *args, **kwargs):
50 est = BootstrapEstimator(estimator, self._n_bootstrap_samples, self._n_jobs, compute_means=False)
---> 51 est.fit(*args, **kwargs)
52 self._est = est
53

~\AppData\Roaming\Python\Python36\site-packages\econml\bootstrap.py in fit(self, *args, **named_args)
75 *[convertArg(arg, inds) for arg in args],
76 **{arg: convertArg(named_args[arg], inds) for arg in named_args})
---> 77 for obj, inds in zip(self._instances, indices)
78 )
79 return self

~\AppData\Roaming\Python\Python36\site-packages\joblib\parallel.py in call(self, iterable)
1027 # remaining jobs.
1028 self._iterating = False
-> 1029 if self.dispatch_one_batch(iterator):
1030 self._iterating = self._original_iterator is not None
1031

~\AppData\Roaming\Python\Python36\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
817 big_batch_size = batch_size * n_jobs
818
--> 819 islice = list(itertools.islice(iterator, big_batch_size))
820 if len(islice) == 0:
821 return False

~\AppData\Roaming\Python\Python36\site-packages\econml\bootstrap.py in (.0)
75 *[convertArg(arg, inds) for arg in args],
76 **{arg: convertArg(named_args[arg], inds) for arg in named_args})
---> 77 for obj, inds in zip(self._instances, indices)
78 )
79 return self

~\AppData\Roaming\Python\Python36\site-packages\econml\bootstrap.py in (.0)
73 self._instances = Parallel(n_jobs=self._n_jobs, prefer='threads', verbose=3)(
74 delayed(fit)(obj,
---> 75 *[convertArg(arg, inds) for arg in args],
76 **{arg: convertArg(named_args[arg], inds) for arg in named_args})
77 for obj, inds in zip(self._instances, indices)

~\AppData\Roaming\Python\Python36\site-packages\econml\bootstrap.py in convertArg(arg, inds)
70
71 def convertArg(arg, inds):
---> 72 return arg[inds] if arg is not None else None
73 self._instances = Parallel(n_jobs=self._n_jobs, prefer='threads', verbose=3)(
74 delayed(fit)(obj,

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\series.py in getitem(self, key)
906 return self._get_values(key)
907
--> 908 return self._get_with(key)
909
910 def _get_with(self, key):

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\series.py in _get_with(self, key)
941 # (i.e. self.iloc) or label-based (i.e. self.loc)
942 if not self.index._should_fallback_to_positional():
--> 943 return self.loc[key]
944 else:
945 return self.iloc[key]

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\indexing.py in getitem(self, key)
877
878 maybe_callable = com.apply_if_callable(key, self.obj)
--> 879 return self._getitem_axis(maybe_callable, axis=axis)
880
881 def _is_scalar_access(self, key: Tuple):

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1097 raise ValueError("Cannot index with multidimensional key")
1098
-> 1099 return self._getitem_iterable(key, axis=axis)
1100
1101 # nested tuple slicing

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\indexing.py in _getitem_iterable(self, key, axis)
1035
1036 # A collection of keys
-> 1037 keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
1038 return self.obj._reindex_with_indexers(
1039 {axis: [keyarr, indexer]}, copy=True, allow_dups=True

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1252 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
1253
-> 1254 self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
1255 return keyarr, indexer
1256

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1314 with option_context("display.max_seq_items", 10, "display.width", 80):
1315 raise KeyError(
-> 1316 "Passing list-likes to .loc or [] with any missing labels "
1317 "is no longer supported. "
1318 f"The following labels were missing: {not_found}. "

KeyError: "Passing list-likes to .loc or [] with any missing labels is no longer supported. The following labels were missing: Int64Index([45401], dtype='int64'). See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike"

Reproduce the reported X_learner BootstrapInference failure from the example in the issue, then inspect econml/bootstrap.py and econml/inference.py around bootstrap fitting and argument indexing. Check how the pandas Series is indexed during resampling and run the existing inference tests if available. Done means bootstrap inference completes for this X_learner input without the missing-label KeyError.