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Causal inference in cplint
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DOI：10.1016/j.ijar.2017.09.007.png)
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Abstract
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cplint is a suite of programs for reasoning and learning with Probabilistic Logic Programming languages that follow the distribution semantics. In this paper we describe how we have extended cplint to perform causal reasoning. In particular, we consider Pearl's do calculus for models where all the variables are measured. The two cplint modules for inference, PITA and MCINTYRE, have been extended for computing the effect of actions/interventions on these models. We also executed experiments comparing exact and approximate inference with conditional and causal queries, showing that causal inference is often cheaper than conditional inference. (C) 2017 Elsevier Inc. All rights reserved.
Keywords:
Probabilistic Logic Programming
Distribution semantics
Logic programs with annotated disjunctions
ProbLog
Causal inference
Statistical relational artificial intelligence
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