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DoWhy: A library for causal inference
May 2021
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine…
Diverse Counterfactual Explanations (DiCE) for ML
July 2019
DiCE is a Python library to explain an ML model such that the explanation is truthful to the model and yet interpretable to people. This connects to the “Explainable AI systems” theme.