Showing items with similar title or keywords.

• Simplifying Probabilistic Expressions in Causal Inference 

  Tikka, Santtu; Karvanen, Juha (MIT Press, 2017)

  Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identifiable causal effect by an algorithm or ...

• Clustering and Structural Robustness in Causal Diagrams 

  Tikka, Santtu; Helske, Jouni; Karvanen, Juha (JMLR, 2023)

  Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As the number of variables under study ...

• Evolutionary game theory of continuous traits from a causal perspective 

  Lehtonen, Jussi; Otsuka, Jun (The Royal Society Publishing, 2023)

  Modern evolutionary game theory typically deals with the evolution of continuous, quantitative traits under weak selection, allowing the incorporation of rich biological detail and complicated nonlinear interactions. While ...

• Enhancing Identification of Causal Effects by Pruning 

  Tikka, Santtu; Karvanen, Juha (MIT Press, 2018)

  Causal models communicate our assumptions about causes and e ects in real-world phenomena. Often the interest lies in the identification of the e ect of an action which means deriving an expression from the observed ...

• Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation 

  Terziyan, Vagan; Vitko, Oleksandra (Elsevier, 2023)

  Smart manufacturing uses emerging deep learning models, and particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), for different industrial diagnostics tasks, e.g., classification, ...