Abstract: One or more application domain properties are integrated into a machine learning model by obtaining training data for use in training the machine learning model, where the training data includes factual data relating to a particular application, and obtaining, with reference to the training data, unlabeled counterfactual data for the particular application. The method includes imputing one or more labels to the unlabeled counterfactual data using domain knowledge for the particular application to obtain imputed counterfactual data. The domain knowledge includes one or more application domain properties. Further, the method includes training the machine learning model using the training data and the imputed counterfactual data to facilitate generating machine learning model predictions for the particular application in accordance with the one or more application domain properties.

Abstract: A method of using a computing device to self-train a machine learning model with an incomplete dataset including original observational data. The method includes receiving a labeled training data, the labeled training data for training a machine learning model. Counterfactual unlabeled training data is received. One or more labels are predicted for the counterfactual unlabeled training data. The machine learning model is trained based upon the labeled training data, the counterfactual unlabeled training data, and the predicted one or more labels for the unlabeled training data. The machine learning model reduces bias in original observational data. An evaluation of the predicted one or more labels is received based on corresponding artificial intelligence explanations provided by an artificial intelligence explainability model.