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Probabilistic logic programs are logic programs where some facts hold with aspecified probability. Here, we investigate these programs with a causalframework that allows counterfactual queries. Learning the program structurefrom observational data is usually done through heuristic search relying onstatistical tests. However, these statistical tests lack information about thecausal mechanism generating the data, which makes it unfeasible to use theresulting programs for counterfactual reasoning. To address this, we propose alanguage fragment that allows reconstructing a program from its induceddistribution. This further enables us to learn programs supportingcounterfactual queries.

"Would life be more interesting if I were in AI?" Answering Counterfactuals based on Probabilistic Inductive Logic Programming