when use interpret function of SingleTreeCateInterpreter， some error occured

i am using DML to inference， the DML model has finished， but what interpret the model, some error occured as followed:
InvalidParameterError: The 'criterion' parameter of DecisionTreeRegressor must be a str among {'friedman_mse', 'absolute_error', 'poisson', 'squared_error'}. Got 'mse' instead.

the coed as followed：

**analytic_data = data[data['session_num'].isin([exp_session, ctr_session])]
y = analytic_data['ret_1'].astype("float").values
X = analytic_data.drop(['t0.member_id', 'session_num', 'ret_1'], axis=1)
analytic_data['Treatment'] = analytic_data.apply(lambda row: 0 if row['session_num'] == ctr_session else 1, axis=1)
T = analytic_data['Treatment'].astype("float").values

X_train, X_test, y_train, y_test, T_train, T_test = train_test_split(X, y, T, test_size=0.5,
random_state=101)
est = CausalForestDML(
model_y=RandomForestRegressor(n_jobs=-1, max_depth=10, n_estimators=20),
model_t=RandomForestRegressor(n_jobs=-1, max_depth=10, n_estimators=20),
n_estimators=100, max_depth=8, min_samples_leaf=20000
)
est.fit(Y=y_train, T=T_train, X=X_train, W=None)

intrp = SingleTreeCateInterpreter(max_depth=3, random_state=30)
intrp.interpret(est, X_test)
plt.figure(figsize=(15, 8))
intrp.plot(feature_names=X_test.columns, fontsize=12)**

and the whole error text as followed：

InvalidParameterError Traceback (most recent call last)
Cell In[15], line 2
1 intrp = SingleTreeCateInterpreter(max_depth=3, random_state=30)
----> 2 intrp.interpret(est, X_test)
3 plt.figure(figsize=(15, 8))
4 intrp.plot(feature_names=X_test.columns, fontsize=12)

File ~/anaconda3/lib/python3.10/site-packages/econml/cate_interpreter/interpreters.py:193, in SingleTreeCateInterpreter.interpret(self, cate_estimator, X)
181 self.tree_model = DecisionTreeRegressor(criterion=self.criterion,
182 splitter=self.splitter,
183 max_depth=self.max_depth,
(...)
189 max_leaf_nodes=self.max_leaf_nodes,
190 min_impurity_decrease=self.min_impurity_decrease)
191 y_pred = cate_estimator.const_marginal_effect(X)
--> 193 self.tree_model_.fit(X, y_pred.reshape((y_pred.shape[0], -1)))
194 paths = self.tree_model_.decision_path(X)
195 node_dict = {}

File ~/anaconda3/lib/python3.10/site-packages/sklearn/tree/_classes.py:1247, in DecisionTreeRegressor.fit(self, X, y, sample_weight, check_input)
1218 def fit(self, X, y, sample_weight=None, check_input=True):
1219 """Build a decision tree regressor from the training set (X, y).
1220
1221 Parameters
(...)
1244 Fitted estimator.
1245 """
-> 1247 super().fit(
1248 X,
1249 y,
1250 sample_weight=sample_weight,
1251 check_input=check_input,
1252 )
1253 return self

File ~/anaconda3/lib/python3.10/site-packages/sklearn/tree/_classes.py:177, in BaseDecisionTree.fit(self, X, y, sample_weight, check_input)
176 def fit(self, X, y, sample_weight=None, check_input=True):
--> 177 self._validate_params()
178 random_state = check_random_state(self.random_state)
180 if check_input:
181 # Need to validate separately here.
182 # We can't pass multi_output=True because that would allow y to be
183 # csr.

File ~/anaconda3/lib/python3.10/site-packages/sklearn/base.py:581, in BaseEstimator._validate_params(self)
573 def _validate_params(self):
574 """Validate types and values of constructor parameters
575
576 The expected type and values must be defined in the _parameter_constraints
(...)
579 accepted constraints.
580 """
--> 581 validate_parameter_constraints(
582 self._parameter_constraints,
583 self.get_params(deep=False),
584 caller_name=self.class.name,
585 )

File ~/anaconda3/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:97, in validate_parameter_constraints(parameter_constraints, params, caller_name)
91 else:
92 constraints_str = (
93 f"{', '.join([str(c) for c in constraints[:-1]])} or"
94 f" {constraints[-1]}"
95 )
---> 97 raise InvalidParameterError(
98 f"The {param_name!r} parameter of {caller_name} must be"
99 f" {constraints_str}. Got {param_val!r} instead."
100 )

InvalidParameterError: The 'criterion' parameter of DecisionTreeRegressor must be a str among {'friedman_mse', 'absolute_error', 'poisson', 'squared_error'}. Got 'mse' instead.

Start in econml/cate_interpreter/_interpreters.py at SingleTreeCateInterpreter.interpret, where DecisionTreeRegressor is constructed before fitting the estimated effects. Check the default criterion against the supported scikit-learn values, then rerun the reported SingleTreeCateInterpreter example and confirm that interpret and plot complete without the parameter error.