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.
If the confidence intervals are misspecified, e.g., greater lower bound than upper bound, the method threw an error before. This, however, can sometimes happen due to precision errors in some algorithms and lead to random build fails. This change fixes the issue and ignores invalid intervals accordingly.
If the confidence intervals are misspecified, e.g., greater lower bound than upper bound, the method threw an error before. This, however, can sometimes happen due to precision errors in some algorithms and lead to random build fails. This change fixes the issue and ignores invalid intervals accordingly.