Interpretable instance disease prediction based on causal feature selection and effect analysis.

Background: In the big wave of artificial intelligence sweeping the world, machine learning has made great achievements in healthcare in the past few years, however, these methods are only based on correlation, not causation. The particularities of the healthcare determines that the research method must comply with the causality norm, otherwise the wrong intervention measures may bring the patients a lifetime of misfortune.

Methods: We propose a two-stage prediction method (instance feature selection prediction and causal effect analysis) for instance disease prediction. Feature selection is based on the counterfactual and uses the reinforcement learning framework to design an interpretable qualitative instance feature selection prediction. The model is composed of three neural networks (counterfactual prediction network, fact prediction network and counterfactual feature selection network), and the actor-critical method is used to train the network. Then we take the counterfactual prediction network as a structured causal model and improve the neural network attribution algorithm based on gradient integration to quantitatively calculate the causal effect of selection features on the output results.

Results: The results of our experiments on synthetic data, open source data and real medical data show that our proposed method can provide qualitative and quantitative causal explanations for the model while giving prediction results.

Conclusions: The experimental results demonstrate that causality can further explore more essential relationships between variables and the prediction method based on causal feature selection and effect analysis can build a more reliable disease prediction model.