Models of Variability in Probabilistic Causal Judgments.

Ivar Kolvoort, Zachary J Davis, Bob Rehder, Leendert van Maanen

Abstract read

In one paragraph

Article in Computational brain & behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Magic Act in Causal Reasoning: Making Markov Violations Disappear.Entropy (Basel, Switzerland) · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ivar KolvoortAmsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-7072-4392

Zachary J DavisDepartment of Psychology, New York University, New York, USA.

Bob RehderDepartment of Psychology, New York University, New York, USA.

Leendert van MaanenDepartment of Experimental Psychology, Utrecht University, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-024-00223-7.

Indexed as

Causal judgmentCausal reasoningCognitive modelingRepeated-measuresResponse distributionsSampling

Identifiers

PMID42459223

DOI10.1007/s42113-024-00223-7

PMCPMC13298624

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.