Alessandro Antonucci

2024

1. Journal

   Efficient Computation of Counterfactual Bounds

   Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, and Dario Azzimonti

   International Journal of Approximate Reasoning, Aug 2024

   Synergies between Machine Learning and Reasoning

   Abs arXiv Bib HTML

   We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes. The question we want to answer is how we can compute bounds for partially identifiable counterfactual queries from such an input. We start by giving a map from structural casual models to credal networks. This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models. Exact computation is going to be inefficient in general given that, as we show, causal inference is NP-hard even on polytrees. We target then approximate bounds via a causal EM scheme. We evaluate their accuracy by providing credible intervals on the quality of the approximation; we show through a synthetic benchmark that the EM scheme delivers accurate results in a fair number of runs. In the course of the discussion, we also point out what seems to be a neglected limitation to the trending idea that counterfactual bounds can be computed without knowledge of the structural equations. We also present a real case study on palliative care to show how our algorithms can readily be used for practical purposes.

   @article{zaffalon2023b,
     bibtex_show = true,
     title = {Efficient Computation of Counterfactual Bounds},
     author = {Zaffalon, Marco and Antonucci, Alessandro and Cabañas, Rafael and Huber, David and Azzimonti, Dario},
     year = {2024},
     volume = {171},
     month = aug,
     journal = {International Journal of Approximate Reasoning},
     pages = {109111},
     note = {Synergies between Machine Learning and Reasoning},
     issn = {0888-613X},
     doi = {10.1016/j.ijar.2023.109111}
   }

2023

1. Journal

   Approximating counterfactual bounds while fusing observational, biased and randomised data sources

   Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, and David Huber

   International Journal of Approximate Reasoning, Nov 2023

   Abs arXiv Bib HTML

   We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to approximate the bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can address the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness of our approach, while hinting at the benefits of fusing heterogeneous data sources to get informative outcomes in case of partial identifiability.

   @article{zaffalon2023a,
     bibtex_show = true,
     title = {Approximating counterfactual bounds while fusing observational, biased and randomised data sources},
     author = {Zaffalon, Marco and Antonucci, Alessandro and Cabañas, Rafael and Huber, David},
     year = {2023},
     month = nov,
     journal = {International Journal of Approximate Reasoning},
     doi = {10.1016/j.ijar.2023.109023},
     pages = {109023},
     volume = {162}
   }