diff --git a/econml/inference/_inference.py b/econml/inference/_inference.py
index 5c59103de..1fff3e906 100644
--- a/econml/inference/_inference.py
+++ b/econml/inference/_inference.py
@@ -1179,8 +1179,8 @@ class PopulationSummaryResults:
 
     """
 
-    def __init__(self, pred, pred_stderr, mean_pred_stderr, d_t, d_y, alpha, value, decimals, tol,
-                 output_names=None, treatment_names=None):
+    def __init__(self, pred, pred_stderr, mean_pred_stderr, d_t, d_y, alpha=0.1,
+                 value=0, decimals=3, tol=0.001, output_names=None, treatment_names=None):
         self.pred = pred
         self.pred_stderr = pred_stderr
         self.mean_pred_stderr = mean_pred_stderr
@@ -1237,13 +1237,13 @@ def stderr_mean(self):
             raise AttributeError("Only point estimates are available!")
         return np.sqrt(np.mean(self.pred_stderr**2, axis=0))
 
-    def zstat(self, *, value=0):
+    def zstat(self, *, value=None):
         """
         Get the z statistic of the mean point estimate of each treatment on each outcome for sample X.
 
         Parameters
         ----------
-        value: optinal float (default=0)
+        value: optional float (default=0)
             The mean value of the metric you'd like to test under null hypothesis.
 
         Returns
@@ -1258,13 +1258,13 @@ def zstat(self, *, value=0):
         zstat = (self.mean_point - value) / self.stderr_mean
         return zstat
 
-    def pvalue(self, *, value=0):
+    def pvalue(self, *, value=None):
         """
         Get the p value of the z test of each treatment on each outcome for sample X.
 
         Parameters
         ----------
-        value: optinal float (default=0)
+        value: optional float (default=0)
             The mean value of the metric you'd like to test under null hypothesis.
 
         Returns
@@ -1275,10 +1275,11 @@ def pvalue(self, *, value=0):
             the corresponding singleton dimensions in the output will be collapsed
             (e.g. if both are vectors, then the output of this method will be a scalar)
         """
+        value = self.value if value is None else value
         pvalue = norm.sf(np.abs(self.zstat(value=value)), loc=0, scale=1) * 2
         return pvalue
 
-    def conf_int_mean(self, *, alpha=.1):
+    def conf_int_mean(self, *, alpha=None):
         """
         Get the confidence interval of the mean point estimate of each treatment on each outcome for sample X.
 
@@ -1323,7 +1324,7 @@ def std_point(self):
         """
         return np.std(self.pred, axis=0)
 
-    def percentile_point(self, *, alpha=.1):
+    def percentile_point(self, *, alpha=None):
         """
         Get the confidence interval of the point estimate of each treatment on each outcome for sample X.
 
@@ -1346,7 +1347,7 @@ def percentile_point(self, *, alpha=.1):
         upper_percentile_point = np.percentile(self.pred, (1 - alpha / 2) * 100, axis=0)
         return lower_percentile_point, upper_percentile_point
 
-    def conf_int_point(self, *, alpha=.1, tol=.001):
+    def conf_int_point(self, *, alpha=None, tol=None):
         """
         Get the confidence interval of the point estimate of each treatment on each outcome for sample X.
 
@@ -1389,7 +1390,7 @@ def stderr_point(self):
         """
         return np.sqrt(self.stderr_mean**2 + self.std_point**2)
 
-    def summary(self, alpha=0.1, value=0, decimals=3, tol=0.001, output_names=None, treatment_names=None):
+    def summary(self, alpha=None, value=None, decimals=None, tol=None, output_names=None, treatment_names=None):
         """
         Output the summary inferences above.
 
diff --git a/econml/tests/test_inference.py b/econml/tests/test_inference.py
index 6aebd7412..d977ec1c1 100644
--- a/econml/tests/test_inference.py
+++ b/econml/tests/test_inference.py
@@ -288,6 +288,18 @@ def test_can_summarize(self):
             inference=BootstrapInference(5)
         ).summary(1)
 
+    def test_alpha(self):
+        Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
+        est = LinearDML(model_y=LinearRegression(), model_t=LinearRegression())
+        est.fit(Y, T, X=X, W=W)
+
+        # ensure alpha is passed
+        lb, ub = est.const_marginal_ate_interval(X, alpha=1)
+        assert (lb == ub).all()
+
+        lb, ub = est.const_marginal_ate_interval(X)
+        assert (lb != ub).all()
+
     def test_inference_with_none_stderr(self):
         Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
         est = DML(model_y=LinearRegression(),