Abstract: An automated machine learning approach and toolkit is developed for evaluating the causal impact of an event. This approach includes data generation, optimal model selection, model stability evaluation and model explanation. An example approach includes: generating predictive output data of physical geospatial objects is proposed whereby a first data set representative of geospatial event-based data and a second data set representative of the characteristics of the physical geospatial objects are spatially joined together and utilized to generate a causal graph data model that is then provided for at least one of a trained regression machine learning model, a trained causal machine learning model, and a trained similarity machine learning model to generate the predictive output data representative of event-adjusted characteristics of the physical geospatial objects.

Abstract: Differential private dictionary learning privatizes input data by training an autoencoder to learn a dictionary, the autoencoder including an encoder and a decoder, and weights of channels in a layer in the decoder defining dictionary atoms forming the dictionary; inputting the input data to the trained autoencoder; projecting, using the encoder, the input data on the learned dictionary to generate a sparse representation of the input data, the sparse representation including coefficients for each dictionary atom; adding noise to the sparse representation to generate a noisy sparse representation; and mapping, using the decoder, the noisy sparse representation to a reconstructed differentially private output.