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Found 12 papers, 9 papers with code
Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems
Causal and counterfactual reasoning are emerging directions in data science that allow us to reason about hypothetical scenarios.
Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Efficient Computation of Counterfactual Bounds
This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models.
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation.
Diversity and Generalization in Neural Network Ensembles
In this work, we combine and expand previously published results in a theoretically sound framework that describes the relationship between diversity and ensemble performance for a wide range of ensemble methods.
CREPO: An Open Repository to Benchmark Credal Network Algorithms
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.
Causal Expectation-Maximisation
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.
Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables.
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
InferPy is a Python package for probabilistic modeling with deep neural networks.
Probabilistic Models with Deep Neural Networks
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling.
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data.
