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Models of Causal Belief Systems and Misperceptions: Experimental Tests

Last registered on June 03, 2026

View Trial History

Pre-Trial

Trial Information

General Information

Title

Models of Causal Belief Systems and Misperceptions: Experimental Tests

RCT ID

AEARCTR-0018759

Initial registration date

May 27, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

June 03, 2026, 9:50 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

United States of America

Region

Primary Investigator

Name

Sandro Ambuehl

Affiliation

University of Zurich

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Jindi Huang

PI Affiliation

University of Zurich

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-05-28

End date

2026-09-01

Keywords

Lab

Additional Keywords

Misspecified models, causal cognition, mental models, DAGs, belief updating, structure learning

JEL code(s)

C91, D83, D91

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We experimentally test whether individuals' causal belief systems are well described by the workhorse model of the literature on causal cognition and misspecified mental models: Causal Bayesian Networks (CBN). We ask two questions. First, are belief systems rationalizable by the theory that individuals process data through the lens of a CBN or a distribution over CBNs? Second, do individuals' beliefs and choices exhibit the comparative statics predicted by the literature on misspecified learning? Participants interact with computer-generated systems involving binary variables, make incentivized choices, and report beliefs about causal relationships.

External Link(s)

Registration Citation

Citation

Ambuehl, Sandro and Jindi Huang. 2026. "Models of Causal Belief Systems and Misperceptions: Experimental Tests." AEA RCT Registry. June 03. https://doi.org/10.1257/rct.18759-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Participants complete an online experiment with repeated computer-generated decision environments involving three binary variables: an action, an intermediate variable or signal, and a payoff-relevant outcome. Across treatments, the environments vary the causal and informational structure of these variables. Participants make incentivized choices and report beliefs about causal relationships among the variables. See the attached document for rationale and details.

Intervention (Hidden)

Intervention Start Date

2026-05-28

Intervention End Date

2026-09-01

Primary Outcomes

Primary Outcomes (end points)

Primary outcomes are causal belief systems and action frequencies. Belief outcomes include elicited causal-effect beliefs, including beliefs that fix the third variable interventionally, and the graphs participants draw to represent causal influences. Action outcomes are P(A=1) in reverse-causality and two-variable tasks, P(A=1 | X=0) and P(A=1 | X=1) in confounded-choice tasks, choices in the last 30 of 80 rounds, and corresponding high-stakes WTP/WTA choices.

Primary Outcomes (explanation)

Action frequencies summarize how often participants choose the relevant action in each environment, including state-contingent action rates when X is observed before choice. Belief outcomes measure whether participants perceive causal effects that are absent in the true DGP and whether their reported belief systems satisfy the restrictions implied by single-DAG or DAG-mixture interpretations of their observed histories.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Participants are assigned to online decision environments designed to study causal belief formation and repeated choice. The design includes reverse-causality environments, confounded-choice environments, and two-variable calibration environments. The main comparisons examine how beliefs and choices vary with the environment's causal structure, noise, display order, and signal informativeness. See the attached document for rationale and details.

The main treatment comparisons concern the effect of increasing noise in the DGP on the action frequency and WTP/WTA within each decision environment. Each environment features a high-noise condition and a low-noise condition.

Experimental Design Details

Randomization Method

Done by the survey script

Randomization Unit

Individual

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

There are 11 within-subject treatment comparisons. For 7 of them, we will enlist 120 subjects each, for the remaining four we will enlist 60 subjects each.

Sample size: planned number of observations

1080 individuals We include a subject in the data analysis only if all of the following conditions are met: 1. the subject's prolific authenticity checks has “high” for both fields, LLM and bots. Subjects with missing fields are excluded, too. 2. the subject does _not_ give a correct answer to the question "How many elements does the largest sporadic simple group have?” 3. the subject has a reCaptcha score of at least 0.7 (based on https://web.archive.org/web/20260316085956/https://researcher-help.prolific.com/en/articles/445222-understanding-the-limitations-of-recaptcha-bot-detection-in-research) 4. the subject does not miss the “Monday” attention check (which terminates the survey) 5. the subject passes the technical check in which a sequence of numbers is displayed in video format The target sample size refers to subjects who satisfy all of the above conditions

Sample size (or number of clusters) by treatment arms

1080 individuals

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB