EconPapers    
Economics at your fingertips  
 

Causality and independence in perfectly adapted dynamical systems

Blom Tineke () and Mooij Joris M. ()
Additional contact information
Blom Tineke: Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
Mooij Joris M.: Korteweg-de Vries Institute, University of Amsterdam, Amsterdam, The Netherlands

Journal of Causal Inference, 2023, vol. 11, issue 1, 35

Abstract: Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium. With the help of the causal ordering algorithm, one can construct graphical representations of dynamical systems that represent the causal relations between the variables and the conditional independences in the equilibrium distribution. We apply these tools to formulate sufficient graphical conditions for identifying perfect adaptation from a set of first-order differential equations. Furthermore, we give sufficient conditions to test for the presence of perfect adaptation in experimental equilibrium data. We apply this method to a simple model for a protein signalling pathway and test its predictions in both simulations and using real-world protein expression data. We demonstrate that perfect adaptation can lead to misleading orientation of edges in the output of causal discovery algorithms.

Keywords: causal ordering; causal discovery; feedback; equilibrium; perfect adaptation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2021-0005 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:11:y:2023:i:1:p:35:n:1

DOI: 10.1515/jci-2021-0005

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2025-03-19
Handle: RePEc:bpj:causin:v:11:y:2023:i:1:p:35:n:1