diff --git a/econml/_ortho_learner.py b/econml/_ortho_learner.py
index e27b47a67..bdb8c898c 100644
--- a/econml/_ortho_learner.py
+++ b/econml/_ortho_learner.py
@@ -192,8 +192,8 @@ def predict(self, X, y, W=None):
          X, y,W=y, Z=None)
 
     >>> nuisance
-    (array([-1.105728... , -1.537566..., -2.451827... , ...,  1.106287...,
-           -1.829662..., -1.782273...], shape=(5000,)),)
+    (array([-1.1057289 , -1.53756637, -2.4518278 , ...,  1.10628792,
+           -1.82966233, -1.78227335], shape=(5000,)),)
     >>> model_list
     [<Wrapper object at 0x...>, <Wrapper object at 0x...>]
     >>> fitted_inds
@@ -475,19 +475,19 @@ def _gen_ortho_learner_model_final(self):
         est.fit(y, X[:, 0], W=X[:, 1:])
 
     >>> est.score_
-    np.float64(0.00756830...)
+    np.float64(0.00756830)
     >>> est.const_marginal_effect()
-    np.float64(1.02364992...)
+    np.float64(1.02364992)
     >>> est.effect()
-    array([1.023649...])
+    array([1.023649])
     >>> est.effect(T0=0, T1=10)
-    array([10.236499...])
+    array([10.236499])
     >>> est.score(y, X[:, 0], W=X[:, 1:])
-    np.float64(0.00727995...)
+    np.float64(0.00727995)
     >>> est.ortho_learner_model_final_.model
     LinearRegression(fit_intercept=False)
     >>> est.ortho_learner_model_final_.model.coef_
-    array([1.023649...])
+    array([1.023649])
 
     The following example shows how to do double machine learning with discrete treatments, using
     the _OrthoLearner:
@@ -533,15 +533,15 @@ def _gen_ortho_learner_model_final(self):
         est.fit(y, T, W=W)
 
     >>> est.score_
-    np.float64(0.00672978...)
+    np.float64(0.00672978)
     >>> est.const_marginal_effect()
-    array([[1.008402...]])
+    array([[1.008402]])
     >>> est.effect()
-    array([1.008402...])
+    array([1.008402])
     >>> est.score(y, T, W=W)
-    np.float64(0.00310431...)
+    np.float64(0.00310431)
     >>> est.ortho_learner_model_final_.model.coef_[0]
-    np.float64(1.00840240...)
+    np.float64(1.00840240)
 
     Attributes
     ----------
diff --git a/econml/dml/_rlearner.py b/econml/dml/_rlearner.py
index f64be2344..56234da5a 100644
--- a/econml/dml/_rlearner.py
+++ b/econml/dml/_rlearner.py
@@ -332,17 +332,17 @@ def _gen_rlearner_model_final(self):
         est.fit(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])
 
     >>> est.const_marginal_effect(np.ones((1,1)))
-    array([0.999631...])
+    array([0.999631])
     >>> est.effect(np.ones((1,1)), T0=0, T1=10)
-    array([9.996314...])
+    array([9.996314])
     >>> est.score(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])
-    9.73638006...e-05
+    9.73638006e-05
     >>> est.rlearner_model_final_.model
     LinearRegression(fit_intercept=False)
     >>> est.rlearner_model_final_.model.coef_
-    array([0.999631...])
+    array([0.999631])
     >>> est.score_
-    9.82623204...e-05
+    9.82623204e-05
     >>> [mdl._model for mdls in est.models_y for mdl in mdls]
     [LinearRegression(), LinearRegression()]
     >>> [mdl._model for mdls in est.models_t for mdl in mdls]
diff --git a/econml/dml/causal_forest.py b/econml/dml/causal_forest.py
index d6ff14eeb..b3f1688d4 100644
--- a/econml/dml/causal_forest.py
+++ b/econml/dml/causal_forest.py
@@ -561,10 +561,10 @@ class CausalForestDML(_BaseDML):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.62769..., 1.64575..., 0.68497...])
+    array([0.62769, 1.64575, 0.68497])
     >>> est.effect_interval(X[:3])
-    (array([0.18798..., 1.17144..., 0.10788...]),
-    array([1.06739..., 2.12006..., 1.26205...]))
+    (array([0.18798, 1.17144, 0.10788]),
+    array([1.06739, 2.12006, 1.26205]))
 
     Attributes
     ----------
diff --git a/econml/dml/dml.py b/econml/dml/dml.py
index 23e47f8ec..df2a59eff 100644
--- a/econml/dml/dml.py
+++ b/econml/dml/dml.py
@@ -500,19 +500,19 @@ class takes as input the parameter `model_t`, which is an arbitrary scikit-learn
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.63382..., 1.78225..., 0.71859...])
+    array([0.63382, 1.78225, 0.71859])
     >>> est.effect_interval(X[:3])
-    (array([0.27937..., 1.27619..., 0.42091...]),
-    array([0.98827... , 2.28831..., 1.01628...]))
+    (array([0.27937, 1.27619, 0.42091]),
+    array([0.98827 , 2.28831, 1.01628]))
     >>> est.coef_
-    array([ 0.42857...,  0.04488..., -0.03317...,  0.02258..., -0.14875...])
+    array([ 0.42857,  0.04488, -0.03317,  0.02258, -0.14875])
     >>> est.coef__interval()
-    (array([ 0.25179..., -0.10558..., -0.16723... , -0.11916..., -0.28759...]),
-    array([ 0.60535...,  0.19536...,  0.10088...,  0.16434..., -0.00990...]))
+    (array([ 0.25179, -0.10558, -0.16723 , -0.11916, -0.28759]),
+    array([ 0.60535,  0.19536,  0.10088,  0.16434, -0.00990]))
     >>> est.intercept_
-    1.01166...
+    1.01166
     >>> est.intercept__interval()
-    (0.87125..., 1.15207...)
+    (0.87125, 1.15207)
     """
 
     def __init__(self, *,
@@ -854,19 +854,19 @@ class LinearDML(StatsModelsCateEstimatorMixin, DML):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.49976..., 1.91673..., 0.70800...])
+    array([0.49976, 1.91673, 0.70800])
     >>> est.effect_interval(X[:3])
-    (array([0.15122..., 1.40182..., 0.40956...]),
-    array([0.84831..., 2.43164..., 1.00645...]))
+    (array([0.15122, 1.40182, 0.40956]),
+    array([0.84831, 2.43164, 1.00645]))
     >>> est.coef_
-    array([ 0.48825...,  0.00104...,  0.00244...,  0.02217..., -0.08472...])
+    array([ 0.48825,  0.00104,  0.00244,  0.02217, -0.08472])
     >>> est.coef__interval()
-    (array([ 0.30470... , -0.13905..., -0.12789..., -0.11513..., -0.22506...]),
-    array([0.67180..., 0.14114..., 0.13278..., 0.15949..., 0.05561...]))
+    (array([ 0.30470 , -0.13905, -0.12789, -0.11513, -0.22506]),
+    array([0.67180, 0.14114, 0.13278, 0.15949, 0.05561]))
     >>> est.intercept_
-    1.01248...
+    1.01248
     >>> est.intercept__interval()
-    (0.87481..., 1.15015...)
+    (0.87481, 1.15015)
     """
 
     def __init__(self, *,
@@ -1115,19 +1115,19 @@ class SparseLinearDML(DebiasedLassoCateEstimatorMixin, DML):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.50083..., 1.91668..., 0.70388...])
+    array([0.50083, 1.91668, 0.70388])
     >>> est.effect_interval(X[:3])
-    (array([0.14616..., 1.40370..., 0.40675...]),
-    array([0.85550..., 2.42966..., 1.00101...]))
+    (array([0.14616, 1.40370, 0.40675]),
+    array([0.85550, 2.42966, 1.00101]))
     >>> est.coef_
-    array([ 0.49123...,  0.00493... ,  0.00007...,  0.02302..., -0.08484...])
+    array([ 0.49123452,  0.0049377 ,  0.00007303,  0.02302108, -0.08484665])
     >>> est.coef__interval()
-    (array([ 0.31323..., -0.13850..., -0.13720..., -0.11141..., -0.22962...]),
-    array([0.66923..., 0.14837..., 0.13735..., 0.15745..., 0.05992...]))
+    (array([ 0.31323, -0.13850, -0.13720, -0.11141, -0.22962]),
+    array([0.66923, 0.14837, 0.13735, 0.15745, 0.05992]))
     >>> est.intercept_
-    1.01477...
+    1.01477
     >>> est.intercept__interval()
-    (0.87621..., 1.15333...)
+    (0.87621, 1.15333)
     """
 
     def __init__(self, *,
@@ -1373,7 +1373,7 @@ class KernelDML(DML):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.63042..., 1.86101..., 0.74220...])
+    array([0.63042, 1.86101, 0.74220])
     """
 
     def __init__(self, model_y='auto', model_t='auto',
@@ -1597,7 +1597,7 @@ class NonParamDML(_BaseDML):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([0.35318..., 1.28760..., 0.83506...])
+    array([0.35318, 1.28760, 0.83506])
     """
 
     def __init__(self, *,
diff --git a/econml/dr/_drlearner.py b/econml/dr/_drlearner.py
index 256680525..935b5d592 100644
--- a/econml/dr/_drlearner.py
+++ b/econml/dr/_drlearner.py
@@ -520,18 +520,18 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
         est.fit(y, T, X=X, W=None)
 
     >>> est.const_marginal_effect(X[:2])
-    array([[0.516888..., 0.995747...],
-           [0.356386..., 0.671889...]])
+    array([[0.516888, 0.995747],
+           [0.356386, 0.671889]])
     >>> est.effect(X[:2], T0=0, T1=1)
-    array([0.516888..., 0.356386...])
+    array([0.516888, 0.356386])
     >>> est.score_
-    np.float64(2.845660...)
+    np.float64(2.845660)
     >>> est.score(y, T, X=X)
-    np.float64(1.062668...)
+    np.float64(1.062668)
     >>> est.model_cate(T=1).coef_
-    array([ 0.447146..., -0.001025...,  0.018984...])
+    array([ 0.447146, -0.001025,  0.018984])
     >>> est.model_cate(T=2).coef_
-    array([ 0.925064..., -0.012351...,  0.033480...])
+    array([ 0.925064, -0.012351,  0.033480])
     >>> est.cate_feature_names()
     ['X0', 'X1', 'X2']
 
@@ -555,19 +555,19 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
         est.fit(y, T, X=X, W=None)
 
     >>> est.score_
-    np.float64(1.73...)
+    np.float64(1.73)
     >>> est.const_marginal_effect(X[:3])
-    array([[0.68..., 1.10...],
-           [0.56..., 0.79... ],
-           [0.34..., 0.10... ]])
+    array([[0.68151771, 1.10936267],
+           [0.56679957, 0.7941794 ],
+           [0.34171272, 0.1045798 ]])
     >>> est.model_cate(T=2).coef_
-    array([0.74..., 0.        , 0.        ])
+    array([0.74925162, 0.        , 0.        ])
     >>> est.model_cate(T=2).intercept_
-    np.float64(1.9...)
+    np.float64(1.9227731541272008)
     >>> est.model_cate(T=1).coef_
-    array([0.24..., 0.00..., 0.        ])
+    array([0.24572861, 0.00720134, 0.        ])
     >>> est.model_cate(T=1).intercept_
-    np.float64(0.94...)
+    np.float64(0.94)
 
     Attributes
     ----------
@@ -1245,19 +1245,19 @@ class LinearDRLearner(StatsModelsCateEstimatorDiscreteMixin, DRLearner):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([ 0.432365...,  0.359694..., -0.085428...])
+    array([ 0.432365,  0.359694, -0.085428])
     >>> est.effect_interval(X[:3])
-    (array([ 0.084048..., -0.177951... , -0.734747...]),
-    array([0.780683..., 0.897341..., 0.563889...]))
+    (array([ 0.084048, -0.177951 , -0.734747]),
+    array([0.780683, 0.897341, 0.563889]))
     >>> est.coef_(T=1)
-    array([ 0.450666..., -0.008821...,  0.075271...])
+    array([ 0.450666, -0.008821,  0.075271])
     >>> est.coef__interval(T=1)
-    (array([ 0.156245..., -0.252216..., -0.159709...]),
-    array([0.745086..., 0.234572..., 0.310252...]))
+    (array([ 0.156245, -0.252216, -0.159709]),
+    array([0.745086, 0.234572, 0.310252]))
     >>> est.intercept_(T=1)
-    np.float64(0.909121...)
+    np.float64(0.909121)
     >>> est.intercept__interval(T=1)
-    (np.float64(0.668518...), np.float64(1.149723...))
+    (np.float64(0.668518), np.float64(1.149723))
 
     Attributes
     ----------
@@ -1582,17 +1582,17 @@ class SparseLinearDRLearner(DebiasedLassoCateEstimatorDiscreteMixin, DRLearner):
         est.fit(y, T, X=X, W=None)
 
     >>> est.effect(X[:3])
-    array([ 0.43...,  0.35..., -0.08...])
+    array([ 0.43296409,  0.35952902, -0.08460643])
     >>> est.effect_interval(X[:3])
-    (array([-0.01..., -0.26..., -0.81...]), array([0.87..., 0.98..., 0.65...]))
+    (array([-0.01363264, -0.26369244, -0.81995536]), array([0.87956083, 0.98275048, 0.65074251]))
     >>> est.coef_(T=1)
-    array([ 0.44..., -0.00...,  0.07...])
+    array([ 0.44991911, -0.00881137,  0.07449517])
     >>> est.coef__interval(T=1)
-    (array([ 0.19... , -0.24..., -0.17...]), array([0.70..., 0.22..., 0.32...]))
+    (array([ 0.1972161 , -0.24730627, -0.17104381]), array([0.70262212, 0.22968353, 0.32003415]))
     >>> est.intercept_(T=1)
-    np.float64(0.90...)
+    np.float64(0.9091174878444385)
     >>> est.intercept__interval(T=1)
-    (np.float64(0.66...), np.float64(1.14...))
+    (np.float64(0.6687723462464192), np.float64(1.1494626294424575))
 
     Attributes
     ----------
diff --git a/econml/grf/classes.py b/econml/grf/classes.py
index 46d0a22ab..0ef703109 100644
--- a/econml/grf/classes.py
+++ b/econml/grf/classes.py
@@ -949,7 +949,7 @@ class RegressionForest(BaseGRF):
 
         np.set_printoptions(suppress=True)
         np.random.seed(123)
-        X, y = make_regression(n_samples=1000, n_features=4, n_informative=2,
+        X, y = make_regression(n_samples=1000, n_features=4, n_informative=4,
                                random_state=0, shuffle=False)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5)
         regr = RegressionForest(max_depth=None, random_state=0,
@@ -958,9 +958,9 @@ class RegressionForest(BaseGRF):
     >>> regr.fit(X_train, y_train)
     RegressionForest(n_estimators=1000, random_state=0)
     >>> regr.feature_importances_
-    array([0.88..., 0.11..., 0.00..., 0.00...])
+    array([0.2869952 , 0.03651571, 0.59767877, 0.07881031])
     >>> regr.predict(np.ones((1, 4)), interval=True, alpha=.05)
-    (array([[121.0...]]), array([[103.6...]]), array([[138.3...]]))
+    (array([[194.5075591]]), array([[159.10510002]]), array([[229.91001818]]))
 
     References
     ----------
diff --git a/econml/iv/dml/_dml.py b/econml/iv/dml/_dml.py
index 12e690e34..c8f466c24 100644
--- a/econml/iv/dml/_dml.py
+++ b/econml/iv/dml/_dml.py
@@ -344,19 +344,19 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.28039...,  6.02945..., -2.86845...])
+    array([-4.28039,  6.02945, -2.86845])
     >>> est.effect_interval(X[:3])
-    (array([-7.20723... ,  1.75413..., -5.20891...]),
-    array([-1.35355..., 10.30477..., -0.52799...]))
+    (array([-7.20723 ,  1.75413, -5.20891]),
+    array([-1.35355, 10.30477, -0.52799]))
     >>> est.coef_
-    array([ 4.51656...,  0.78512...,  0.23706...,  0.24127..., -0.47167...])
+    array([ 4.51656,  0.78512,  0.23706,  0.24127, -0.47167])
     >>> est.coef__interval()
-    (array([ 3.15599..., -0.35785..., -0.89798..., -0.90529..., -1.62444...]),
-    array([5.87712..., 1.92810..., 1.37211..., 1.38783..., 0.68110...]))
+    (array([ 3.15599, -0.35785, -0.89798, -0.90529, -1.62444]),
+    array([5.87712, 1.92810, 1.37211, 1.38783, 0.68110]))
     >>> est.intercept_
-    -0.13668...
+    -0.13668
     >>> est.intercept__interval()
-    (-1.27031..., 0.99694...)
+    (-1.27031, 0.99694)
     """
 
     def __init__(self, *,
@@ -1143,11 +1143,11 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-3.83456...,  5.32010..., -2.78127...])
+    array([-3.83456,  5.32010, -2.78127])
     >>> est.coef_
-    array([ 4.03977...,  0.89360...,  0.12047...,  0.37971..., -0.66072...])
+    array([ 4.03977,  0.89360,  0.12047,  0.37971, -0.66072])
     >>> est.intercept_
-    -0.18452...
+    -0.18452
 
     """
 
@@ -1536,7 +1536,7 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-5.98684...,  9.03751..., -3.56817...])
+    array([-5.98684,  9.03751, -3.56817])
 
     """
 
diff --git a/econml/iv/dr/_dr.py b/econml/iv/dr/_dr.py
index b84a79cc7..4d48ab73d 100644
--- a/econml/iv/dr/_dr.py
+++ b/econml/iv/dr/_dr.py
@@ -901,7 +901,7 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.15076...,  5.99286..., -2.86512...])
+    array([-4.15076,  5.99286, -2.86512])
     """
 
     def __init__(self, *,
@@ -1390,19 +1390,19 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.27796...,  5.84996..., -2.98291...])
+    array([-4.27796,  5.84996, -2.98291])
     >>> est.effect_interval(X[:3])
-    (array([-7.16134...,  1.71884..., -5.41437...]),
-    array([-1.39458...,  9.98108..., -0.55145...]))
+    (array([-7.16134,  1.71884, -5.41437]),
+    array([-1.39458,  9.98108, -0.55145]))
     >>> est.coef_
-    array([ 4.65222...,  0.93348...,  0.23315...,  0.22842..., -0.42849...])
+    array([ 4.65222,  0.93348,  0.23315,  0.22842, -0.42849])
     >>> est.coef__interval()
-    (array([ 3.40043..., -0.19165..., -0.95122...  , -0.88663...  , -1.56023...]),
-    array([5.90402..., 2.05861... , 1.41753..., 1.34348..., 0.70325...]))
+    (array([ 3.40043, -0.19165, -0.95122  , -0.88663  , -1.56023]),
+    array([5.90402, 2.05861 , 1.41753, 1.34348, 0.70325]))
     >>> est.intercept_
-    -0.12819...
+    -0.12819
     >>> est.intercept__interval()
-    (-1.27151..., 1.01512...)
+    (-1.27151, 1.01512)
     """
 
     def __init__(self, *,
@@ -1759,19 +1759,19 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.23922...,  5.89221..., -3.01203...])
+    array([-4.23922,  5.89221, -3.01203])
     >>> est.effect_interval(X[:3])
-    (array([-6.99782...,  1.96348..., -5.41958...]),
-    array([-1.48062...,  9.82093..., -0.60449...]))
+    (array([-6.99782,  1.96348, -5.41958]),
+    array([-1.48062,  9.82093, -0.60449]))
     >>> est.coef_
-    array([ 4.65817...,  0.94689... ,  0.18314... ,  0.23012..., -0.40375...])
+    array([ 4.65817,  0.94689 ,  0.18314 ,  0.23012, -0.40375])
     >>> est.coef__interval()
-    (array([ 3.51645..., -0.20838..., -0.99568..., -0.89395..., -1.58517...]),
-    array([5.79989..., 2.10218..., 1.36197..., 1.35419..., 0.77767...]))
+    (array([ 3.51645, -0.20838, -0.99568, -0.89395, -1.58517]),
+    array([5.79989, 2.10218, 1.36197, 1.35419, 0.77767]))
     >>> est.intercept_
-    -0.06535...
+    -0.06535
     >>> est.intercept__interval()
-    (-1.20712..., 1.07641...)
+    (-1.20712, 1.07641)
     """
 
     def __init__(self, *,
@@ -2701,7 +2701,7 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.52684...,  6.38767..., -2.67082...])
+    array([-4.52684,  6.38767, -2.67082])
     """
 
     def __init__(self, *,
@@ -3003,19 +3003,19 @@ def true_heterogeneity_function(X):
         est.fit(Y=y, T=T, Z=Z, X=X)
 
     >>> est.effect(X[:3])
-    array([-4.80543...,  6.10553..., -2.94941...])
+    array([-4.80543,  6.10553, -2.94941])
     >>> est.effect_interval(X[:3])
-    (array([-9.20288..., -0.47079..., -6.67443...]),
-    array([-0.40797..., 12.68185...,  0.77560...]))
+    (array([-9.20288, -0.47079, -6.67443]),
+    array([-0.40797, 12.68185,  0.77560]))
     >>> est.coef_
-    array([ 5.52457...,  0.96284...,  0.68162..., -0.16799..., -0.13048...])
+    array([ 5.52457,  0.96284,  0.68162, -0.16799, -0.13048])
     >>> est.coef__interval()
-    (array([ 3.61389..., -0.81869..., -1.12607..., -1.90211..., -1.92346...]),
-    array([7.43525..., 2.74438..., 2.48932..., 1.56613..., 1.66248...]))
+    (array([ 3.61389, -0.81869, -1.12607, -1.90211, -1.92346]),
+    array([7.43525, 2.74438, 2.48932, 1.56613, 1.66248]))
     >>> est.intercept_
-    -0.28950...
+    -0.28950
     >>> est.intercept__interval()
-    (-2.07685..., 1.49784...)
+    (-2.07685, 1.49784)
     """
 
     def __init__(self, *,
diff --git a/econml/metalearners/_metalearners.py b/econml/metalearners/_metalearners.py
index dc6d05338..d0814dc5d 100644
--- a/econml/metalearners/_metalearners.py
+++ b/econml/metalearners/_metalearners.py
@@ -61,7 +61,7 @@ class TLearner(TreatmentExpansionMixin, LinearCateEstimator):
         est.fit(y, T, X=X)
 
     >>> est.effect(X[:3])
-    array([0.58547..., 1.82860..., 0.78379...])
+    array([0.58547, 1.82860, 0.78379])
     """
 
     def __init__(self, *,
@@ -188,7 +188,7 @@ class SLearner(TreatmentExpansionMixin, LinearCateEstimator):
         est.fit(y, T, X=X)
 
     >>> est.effect(X[:3])
-    array([0.23577..., 1.62784... , 0.45946...])
+    array([0.23577, 1.62784 , 0.45946])
     """
 
     def __init__(self, *,
@@ -335,7 +335,7 @@ class XLearner(TreatmentExpansionMixin, LinearCateEstimator):
         est.fit(y, T, X=X)
 
     >>> est.effect(X[:3])
-    array([0.58547..., 1.82860..., 0.78379...])
+    array([0.58547, 1.82860, 0.78379])
     """
 
     def __init__(self, *,
@@ -503,7 +503,7 @@ class DomainAdaptationLearner(TreatmentExpansionMixin, LinearCateEstimator):
         est.fit(y, T, X=X)
 
     >>> est.effect(X[:3])
-    array([0.51237..., 1.99866..., 0.68552...])
+    array([0.51237, 1.99866, 0.68552])
     """
 
     def __init__(self, *,
diff --git a/econml/panel/dml/_dml.py b/econml/panel/dml/_dml.py
index 93a02686b..ebb5a61a3 100644
--- a/econml/panel/dml/_dml.py
+++ b/econml/panel/dml/_dml.py
@@ -464,33 +464,33 @@ class DynamicDML(LinearModelFinalCateEstimatorMixin, _OrthoLearner):
         est.fit(y, T, X=X, W=None, groups=groups, inference="auto")
 
     >>> est.const_marginal_effect(X[:2])
-    array([[-0.168..., -0.096..., -0.105...,  0.042..., -0.208...,
-             0.006...],
-           [-0.092...,  0.326..., -0.013..., -0.237..., -0.141...,
-            -0.264...]])
+    array([[-0.16860367, -0.09616313, -0.10517216,  0.04299467, -0.20880154,
+             0.00661631],
+           [-0.09213123,  0.32634188, -0.01318959, -0.23741629, -0.14191649,
+            -0.26451663]])
     >>> est.effect(X[:2], T0=0, T1=1)
-    array([-0.529..., -0.422...])
+    array([-0.529, -0.422])
     >>> est.effect(X[:2], T0=np.zeros((2, n_periods*T.shape[1])), T1=np.ones((2, n_periods*T.shape[1])))
-    array([-0.529..., -0.422...])
+    array([-0.529, -0.422])
     >>> est.coef_
-    array([[ 0.036...],
-           [ 0.202...],
-           [ 0.044...],
-           [-0.134...],
-           [ 0.032...],
-           [-0.130...]])
+    array([[ 0.03671307],
+           [ 0.20283719],
+           [ 0.04415921],
+           [-0.13462035],
+           [ 0.03211033],
+           [-0.13016613]])
     >>> est.coef__interval()
-    (array([[-0.136...],
-           [ 0.001...],
-           [-0.110...],
-           [-0.370...],
-           [-0.100...],
-           [-0.294...]]), array([[0.210...],
-           [0.404...],
-           [0.199...],
-           [0.101...],
-           [0.164...],
-           [0.034...]]))
+    (array([[-0.13693069],
+           [ 0.00118659],
+           [-0.11086215],
+           [-0.37026662],
+           [-0.10044078],
+           [-0.29480958]]), array([[0.21035682],
+           [0.40448779],
+           [0.19918057],
+           [0.10102593],
+           [0.16466144],
+           [0.03447732]]))
     """
 
     def __init__(self, *,