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Found 7 papers, 3 papers with code
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
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.
Morphology of three-body quantum states from machine learning
The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard.
Quantum Physics Quantum Gases Exactly Solvable and Integrable Systems Nuclear Theory
Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction
In this work, we examine the correlations between LRP heatmap explanations of a neural network trained to predict driving behavior and eye gaze heatmaps of human drivers.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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