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Computational causal discovery: Advantages and assumptions

Zhang, Kun (2022) Computational causal discovery: Advantages and assumptions. THEORIA. An International Journal for Theory, History and Foundations of Science, 37 (1). pp. 75-86. ISSN 0495-4548

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Abstract

I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making things happen: A theory of causal explanation (Woodward, 2003). Making things happen gives an elegant interventionist theory for understanding explanation and causation. The new contribution (Woodward, 2022) relies on that theory and further makes a big step towards empirical inference of causal relations from non-experimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement Woodward's contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders (unmeasured common causes of two measured variables), and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Zhang, Kunkunz1@cmu.edu
Additional Information: ISSN: 0495-4548 (print)
Keywords: causal direction, interventionist theory, linear, non-Gaussian causal model, confounders, faithfulness
Subjects: General Issues > Causation
Specific Sciences > Computation/Information
General Issues > Explanation
Depositing User: Unnamed user with email theoria@ehu.es
Date Deposited: 15 May 2022 03:51
Last Modified: 15 May 2022 03:51
Item ID: 20610
Journal or Publication Title: THEORIA. An International Journal for Theory, History and Foundations of Science
Publisher: Euskal Herriko Unibertsitatea / Universidad del País Vasco
Official URL: https://ojs.ehu.eus/index.php/THEORIA/article/view...
DOI or Unique Handle: https://doi.org/10.1387/theoria.22904
Subjects: General Issues > Causation
Specific Sciences > Computation/Information
General Issues > Explanation
Date: 2022
Page Range: pp. 75-86
Volume: 37
Number: 1
ISSN: 0495-4548
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/20610

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