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Unmixing for Causal Inference: Thoughts on McCaffrey and Danks.

Kun Zhang¹, Madelyn R K Glymour²

¹Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA, kunz1@cmu.edu.

The British Journal for the Philosophy of Science

|December 30, 2020

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This study presents an algorithm to overcome the challenge of identifying causal relationships in functional magnetic resonance (fMRI) data from mixed distributions, demonstrating accurate data separation in tests.

Area of Science:

Neuroimaging
Causal Inference
Machine Learning

Background:

Discovering causal relations in functional magnetic resonance (fMRI) data is complex, especially when data originates from mixed distributions.
McCaffrey and Danks previously suggested this is an insurmountable challenge for fMRI.
Automated causal discovery methods face obstacles like mixed distributions.

Purpose of the Study:

To develop and validate an algorithm capable of identifying causal relationships within fMRI data from mixed distributions.
To challenge the notion that mixed distributions present an impossibility result in causal discovery for fMRI.

Main Methods:

An algorithm was developed to specifically address causal discovery in fMRI data.
The algorithm was designed for distributions commonly assumed in fMRI studies.

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The algorithm's performance was evaluated through testing on mixed distribution data.

Main Results:

The developed algorithm successfully separated data originating from mixed distributions.
Testing demonstrated the algorithm's accuracy in handling complex fMRI data scenarios.
The findings indicate that the problem of mixed distributions is solvable.

Conclusions:

The proposed algorithm effectively addresses the challenge of causal discovery in fMRI data with mixed distributions.
This work reframes the mixed distribution problem not as an impossibility, but as a solvable challenge in automated causal search.
The algorithm shows promise for advancing causal inference in neuroimaging studies.