DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
The issue was that pandas versions prior to 2.0 correctly inferred the data type from an input like np.array([[2.0]], dtype=object) (even though the dtype is object). In contrast, pandas 2.0 does not make this inference. However, the two-dimensional value was unintentional in the first place. It should be np.array([2.0], dtype=object), which is then correctly handled across all versions.
Addresses https://github.com/py-why/dowhy/issues/1020
The issue was that pandas versions prior to 2.0 correctly inferred the data type from an input like
np.array([[2.0]], dtype=object)(even though the dtype is object). In contrast, pandas 2.0 does not make this inference. However, the two-dimensional value was unintentional in the first place. It should benp.array([2.0], dtype=object), which is then correctly handled across all versions.