Causal inference in cplint. 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.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Azzolini, Damiano; Bellodi, Elena; Ferilli, Stefano; Riguzzi, Fabrizio; Zese, Riccardo: Abduction with probabilistic logic programming under the distribution semantics (2022)
- Nguembang Fadja, Arnaud; Riguzzi, Fabrizio; Lamma, Evelina: Learning hierarchical probabilistic logic programs (2021)
- Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
- Pozzato, Gian Luca: Typicalities and probabilities of exceptions in nonmotonic description logics (2019)
- Wielemaker, Jan; Riguzzi, Fabrizio; Kowalski, Robert A.; Lager, Torbjörn; Sadri, Fariba; Calejo, Miguel: Using SWISH to realize interactive web-based tutorials for logic-based languages (2019)
- Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)