---
title: "Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning"
authors:
  - "Chitta Baral"
  - "Matt Hunsaker"
doi: ""
url: "https://ijcai.org/proceedings/2007/37"
pdf: "https://ijcai.org/proceedings/2007/0037.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "243-248"
track: ""
keywords:
---

# Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning

Chitta Baral, Matt Hunsaker

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 243-248.

## Abstract

P-log is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of P-log have been discussed. In this paper we further elaborate on the KR prowess of P-log by showing that: (i) it can be used for causal and counterfactual reasoning and (ii) it provides an elaboration tolerant way for non-naive conditioning.