Causal learning

[Azariagmt]

• Split data into training and hold-out set
• Create a causal graph using all training data and get the insights (this will be considered the ground truth)
• Create new causal graphs using increasing fractions of the data and compare with the ground truth graph
  The comparison can be done with a Jaccard Similarity Index, measuring the intersection and union of the graph edges
• After reaching a stable causal graph, select only variables that point directly to the target variable
• Train one model using all variables and another using only the variables selected by the graph
• Measure how much each of the models overfit the hold-out set created in step 1.