A Strategy for Making Predictions Under Manipulation
Laura E. Brown, Ioannis Tsamardinos · 2008
Our submission for the LOCANET challenge relied on the results and procedures of the first causality challenge, from which the local networks were pruned. Details to the approach used for the first causality challenge are available in the paper for that challenge (available at the DSL website) but a general overview of the method and how the results are used for this task are presented. Preprocessing: The preprocessing was tailored to each data set. For the REGED data set each variable was normalized so its mean was zero and standard deviation was one. For the SIDO data set, the variables were binary and no preprocessing was performed. For the CINA data set, variables that were not binary were treated as continuous and normalized; binary variables were all set to values of zero and one. For the MARTI data set, the preprocessed data by Dr. Guyon available on the challenge website was used. Causal discovery: Once the initial data sets have been pre-processed, the next step of our procedure was to identify the skeleton structure of the Bayesian Network around the target variable recursively using the MMPC algorithm, up to three edges away from the target. This region of interest makes it practical to apply causal algorithms that cannot