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pro vyhledávání: '"Cabañas, Rafael"'
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
Externí odkaz:
http://arxiv.org/abs/2307.16577
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 part
Externí odkaz:
http://arxiv.org/abs/2307.08304
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
Externí odkaz:
http://arxiv.org/abs/2212.02932
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algor
Externí odkaz:
http://arxiv.org/abs/2208.01417
Ensembles are widely used in machine learning and, usually, provide state-of-the-art performance in many prediction tasks. From the very beginning, the diversity of an ensemble has been identified as a key factor for the superior performance of these
Externí odkaz:
http://arxiv.org/abs/2110.13786
Publikováno v:
In International Journal of Approximate Reasoning August 2024 171
Autor:
Cabañas, Rafael, Antonucci, Alessandro
Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to mo
Externí odkaz:
http://arxiv.org/abs/2105.04158
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation. But they often contain latent variables that limit their applic
Externí odkaz:
http://arxiv.org/abs/2011.02912
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model int
Externí odkaz:
http://arxiv.org/abs/2008.00463
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic model
Externí odkaz:
http://arxiv.org/abs/1908.11161