diff --git a/ax/modelbridge/transforms/power_transform_y.py b/ax/modelbridge/transforms/power_transform_y.py
index dca4ac63e3a..a8e79496b3f 100644
--- a/ax/modelbridge/transforms/power_transform_y.py
+++ b/ax/modelbridge/transforms/power_transform_y.py
@@ -17,11 +17,13 @@
 from ax.core.optimization_config import OptimizationConfig
 from ax.core.outcome_constraint import OutcomeConstraint, ScalarizedOutcomeConstraint
 from ax.core.search_space import SearchSpace
+from ax.exceptions.core import DataRequiredError
 from ax.modelbridge.transforms.base import Transform
 from ax.modelbridge.transforms.utils import get_data, match_ci_width_truncated
 from ax.models.types import TConfig
 from ax.utils.common.logger import get_logger
 from ax.utils.common.typeutils import checked_cast_list
+from pyre_extensions import assert_is_instance
 from sklearn.preprocessing import PowerTransformer
 
 if TYPE_CHECKING:
@@ -57,19 +59,29 @@ def __init__(
         modelbridge: modelbridge_module.base.ModelBridge | None = None,
         config: TConfig | None = None,
     ) -> None:
-        assert observations is not None, "PowerTransformY requires observations"
-        if config is None:
-            raise ValueError("PowerTransform requires a config.")
-        # pyre-fixme[6]: Same issue as for LogY
-        metric_names = list(config.get("metrics", []))
-        if len(metric_names) == 0:
-            raise ValueError("Must specify at least one metric in the config.")
-        # pyre-fixme[4]: Attribute must be annotated.
-        self.clip_mean = config.get("clip_mean", True)
-        # pyre-fixme[4]: Attribute must be annotated.
-        self.metric_names = metric_names
+        """Initialize the ``PowerTransformY`` transform.
+
+        Args:
+            search_space: The search space of the experiment. Unused.
+            observations: A list of observations from the experiment.
+            modelbridge: The `ModelBridge` within which the transform is used. Unused.
+            config: A dictionary of options to control the behavior of the transform.
+                Can contain the following keys:
+                - "metrics": A list of metric names to apply the transform to. If
+                    omitted, all metrics found in `observations` are transformed.
+                - "clip_mean": Whether to clip the mean to the image of the transform.
+                    Defaults to True.
+        """
+        if observations is None or len(observations) == 0:
+            raise DataRequiredError("PowerTransformY requires observations.")
+        # pyre-fixme[9]: Can't annotate config["metrics"] properly.
+        metric_names: list[str] | None = config.get("metrics", None) if config else None
+        self.clip_mean: bool = (
+            assert_is_instance(config.get("clip_mean", True), bool) if config else True
+        )
         observation_data = [obs.data for obs in observations]
         Ys = get_data(observation_data=observation_data, metric_names=metric_names)
+        self.metric_names: list[str] = list(Ys.keys())
         # pyre-fixme[4]: Attribute must be annotated.
         self.power_transforms = _compute_power_transforms(Ys=Ys)
         # pyre-fixme[4]: Attribute must be annotated.
diff --git a/ax/modelbridge/transforms/tests/test_power_y_transform.py b/ax/modelbridge/transforms/tests/test_power_y_transform.py
index 97d5972fa69..15eb4f7fa0b 100644
--- a/ax/modelbridge/transforms/tests/test_power_y_transform.py
+++ b/ax/modelbridge/transforms/tests/test_power_y_transform.py
@@ -72,13 +72,12 @@ def test_Init(self) -> None:
             "search_space": None,
             "observations": self.observations[:2],
         }
-        # Test error for not specifying a config
-        with self.assertRaises(ValueError):
-            PowerTransformY(**shared_init_args)
-        # Test error for not specifying at least one metric
-        with self.assertRaises(ValueError):
-            PowerTransformY(**shared_init_args, config={})
-        # Test default init
+        # Init without a config.
+        t = PowerTransformY(**shared_init_args)
+        self.assertTrue(t.clip_mean)
+        self.assertEqual(t.metric_names, ["m1", "m2"])
+
+        # Test init with config.
         for m in ["m1", "m2"]:
             tf = PowerTransformY(**shared_init_args, config={"metrics": [m]})
             # tf.power_transforms should only exist for m and be a PowerTransformer
@@ -202,6 +201,12 @@ def test_TransformAndUntransformOneMetric(self) -> None:
         )[0]
         cov_results = np.array(transformed_obsd_nan.covariance)
         self.assertTrue(np.all(np.isnan(np.diag(cov_results))))
+        untransformed = pt._untransform_observation_data([transformed_obsd_nan])[0]
+        self.assertTrue(
+            np.array_equal(
+                untransformed.covariance, self.obsd_nan.covariance, equal_nan=True
+            )
+        )
 
     def test_TransformAndUntransformAllMetrics(self) -> None:
         pt = PowerTransformY(
diff --git a/ax/modelbridge/transforms/utils.py b/ax/modelbridge/transforms/utils.py
index 614a33e69ef..e3db346eecb 100644
--- a/ax/modelbridge/transforms/utils.py
+++ b/ax/modelbridge/transforms/utils.py
@@ -113,14 +113,17 @@ def match_ci_width_truncated(
     See log_y transform for the original. Here, bounds are forced to lie
     within a [lower_bound, upper_bound] interval after transformation."""
     fac = norm.ppf(1 - (1 - level) / 2)
-    d = fac * np.sqrt(variance)
     if clip_mean:
         mean = np.clip(mean, lower_bound + margin, upper_bound - margin)
-    right = min(mean + d, upper_bound - margin)
-    left = max(mean - d, lower_bound + margin)
-    width_asym = transform(right) - transform(left)
     new_mean = transform(mean)
-    new_variance = float("nan") if isnan(variance) else (width_asym / 2 / fac) ** 2
+    if isnan(variance):
+        new_variance = variance
+    else:
+        d = fac * np.sqrt(variance)
+        right = min(mean + d, upper_bound - margin)
+        left = max(mean - d, lower_bound + margin)
+        width_asym = transform(right) - transform(left)
+        new_variance = (width_asym / 2 / fac) ** 2
     return new_mean, new_variance