Correct default value of alphas (#476)

The PopulationSummaryResults class should to default to self.alpha when the alpha parameter is not passed. This is not the case currently since some methods use the statement 'alpha = self.alpha if alpha is None else alpha', however alpha's default value in the methods is .1, thus self.alpha will be ignored even if the client does not pass the alpha parameter.

Without the fix ate_interval(), marginal_ate_interval() and const_marginal_ate_interval() all ignore the alpha parameter.

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.
 

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(),