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

• URL: http://arxiv.org/abs/2308.15883v1
• Date: Wed, 30 Aug 2023 09:03:45 GMT
• Title: "Would life be more interesting if I were in AI?" Answering Counterfactuals based on Probabilistic Inductive Logic Programming
• Authors: Kilian R\"uckschlo{\ss} (Ludwig-Maximilians Universit\"at), Felix Weitk\"amper (Ludwig-Maximilians Universit\"at)
• Abstract summary: We investigate probabilistic logic programs with a causal framework that allows counterfactual queries. Learning the program structure from observational data is usually done through search relying on statistical tests. We propose a language fragment that allows reconstructing a program from its induced distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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