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

TL;DR

This paper introduces a causal framework for probabilistic logic programs, enabling counterfactual reasoning by reconstructing programs from their distributions, which improves the ability to perform counterfactual queries.

Contribution

It proposes a new language fragment that allows learning probabilistic logic programs supporting counterfactual reasoning from observational data.

Findings

A new causal framework for probabilistic logic programs.

A method to reconstruct programs supporting counterfactual queries.

Enhanced capability for counterfactual reasoning in probabilistic logic programming.

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.