Content distribution based on causal relationship data

A method for training a machine learning model implemented by a computing device including at least one processor and at least one memory, the method comprising:

• obtaining, by the computing device, a training set including data with one or more of content data, interaction data, profile data, and factor data;
• generating, by the computing device, shadow data corresponding to the data by duplicating the data and randomly reassigning feature values of the duplicated data;
• selecting, by the computing device, one or more prominent features by comparing the data and the shadow data;
• computing, using a machine learning model, causal relationship data for the data by optimizing a plurality of edges on one or more graphs based on the one or more prominent features;
• and
• training, using the training set, the machine learning model to predict causal relationships based on the one or more graphs.

An apparatus for data processing, the apparatus comprising:

• at least one processor;
• at least one memory storing instructions executable by the at least one processor;
• a feature selection component comprising feature selection parameters stored in the at least one memory, wherein the feature selection component is configured to generate shadow data and select one or more prominent features by comparing data and the shadow data;
• and
• a machine learning model comprising machine learning parameters stored in the at least one memory and trained to compute causal relationship data for the data by optimizing a plurality of edges on one or more graphs based on the one or more prominent features.