Causing (CAUSal INterpretation using Graphs) is a multivariate graphic analysis tool for bringing transparency to neural networks. It explains causality and helps researchers and developers interpret the causal effects of a given equation system to ensure fairness. Developers can input data and a model describing the dependencies between the variables within the data set into Causing, and Causing will output a colored graph of quantified effects acting between the model’s variables. In addition, it also allows developers to estimate these effects to validate whether data fits a model.
Thank you,
The PyTorch team