Learning stable and predictive structures in kinetic systems: Benefits of a causal approach

DOI10.48550/arXiv.1810.11776zbMath1456.70002arXiv1810.11776OpenAlexW2990645394WikidataQ91496033 ScholiaQ91496033MaRDI QIDQ144723

Niklas Pfister, Jonas Peters, Stefan Bauer, Stefan Bauer, Jonas Peters, Niklas Pfister

Publication date: 28 October 2018

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.11776

Mathematics Subject Classification ID

Kinematics of mechanisms and robots (70B15)

Related Items (6)

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables ⋮ Deconfounding and Causal Regularisation for Stability and External Validity ⋮ Kernel Ordinary Differential Equations ⋮ Stabilizing Variable Selection and Regression ⋮ CausalKinetiX ⋮ Foundations of structural causal models with cycles and latent variables

Uses Software

• SUNDIALS
• fda (R)
• Data2Dynamics

Cites Work

• SUNDIALS
• Causal inference using invariant prediction: identification and confidence intervals
• Stability
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Functional data analysis.
• Stability Selection
• Iterative random forests to discover predictive and stable high-order interactions
• Parameter Estimation for Differential Equations: a Generalized Smoothing Approach
• Causal Inference for Statistics, Social, and Biomedical Sciences
• The Probability Approach in Econometrics
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