"Would life be more interesting if I were in AI?" Answering
  Counterfactuals based on Probabilistic Inductive Logic Programming
Kilian Rückschloß Felix Weitkämper
Probabilistic logic programs are logic programs where some facts hold with a
specified probability. Here, we investigate these programs with a causal
framework that allows counterfactual queries. Learning the program structure
from observational data is usually done through heuristic search relying on
statistical tests. However, these statistical tests lack information about the
causal mechanism generating the data, which makes it unfeasible to use the
resulting programs for counterfactual reasoning. To address this, we propose a
language fragment that allows reconstructing a program from its induced
distribution. This further enables us to learn programs supporting
counterfactual queries.