From Associations to Theories to an Atlas: Building a Cumulative Causal Science of Autism
Year: 2026
Type: preprint
Abstract: Stakeholders are concerned that we have not made as much usable progress in autism research as we would want, given that approximately 100,000 papers have been published concerning the condition over the last 100 years. In this chapter, we identify that usable knowledge in the clinical, policy and support realms refers to causal knowledge. In the first half, we diagnose why this causal knowledge is particularly challenging to achieve within the constraints of psychological and cognitive-neuroscientific research, fields in which we typically have to rely on correlation rather than experimental manipulation. The gap between correlation (the information we have access to) and causation (the conclusions we need to make) is related to a large degree to addressing the likelihood of alternative causal paths that are able to explain the observed correlations, and falsifying these alternative causal paths is a primary key to closing the gap and arriving at causal conclusions. In the second half, we prescribe a falsification-centric approach toward approximating causality combined with a quantification of remaining paths. This aggregation will result in an atlas of evidence for and against causal claims in the condition. This approach focuses on empirical opportunities to empirically falsify “unnatural” constructs important to the various autism causal hypotheses as well as falsification of candidate causal relationships between those constructs. Moreover, we are able to accumulate prior results from the 100,000+ papers prior to planning additional data collection in a way that is independent of contemporaneous or even current assumptions in autism research and in a way that circumvents the working memory limitations of any particular researcher, clinician or advocate.
Source: PsyArXiv (OSF Preprints)
Author: Joshua Ewen
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225659566
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