Evidence map›Paper›PMID 42187992›Full record

ArticleEntropy (Basel, Switzerland)2026

Causal Structure Learning Assumptions Shape Counterfactual Safety: Expert-Guided Constraints vs. Data-Driven DAGs with Probabilistic Logic Twin Networks.

Héctor Avilés, Ingridh Gracia, Rafael Kiesel, Verónica Rodríguez, Rubén Machucho, Alberto Reyes, Marco Negrete, Gabriel Ramírez, Nicolás Luévano, Myriam Pequeño and 2 more

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Héctor AvilésDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.ORCID 0000-0001-5310-3474
Ingridh GraciaDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.ORCID 0009-0004-7101-6656
Rafael KieselInstitute of Computational Logic, Vienna University of Technology, 1040 Vienna, Austria.
Verónica RodríguezComputer Science Institute, Technological University of the Mixteca, Huajuapan de León 69004, Mexico.ORCID 0000-0002-5976-9338
Rubén MachuchoDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.ORCID 0000-0002-5731-6677
Alberto ReyesControl, Electronics and Communications Department, National Institute of Electricity and Clean Energies, Cuernavaca 62490, Mexico.ORCID 0000-0002-8509-6974
Marco NegreteFaculty of Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico.
Gabriel RamírezCenter for Research and Advanced Studies, The National Polytechnic Institute, Tamaulipas Campus, Ciudad Victoria 87138, Mexico.
Nicolás LuévanoDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.
Myriam PequeñoDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.
Jesús MedranoDepartment of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.ORCID 0009-0002-0355-7755
Felix WeitkämperStatistical and Symbolic Artificial Intelligence Group, German University of Digital Science, 14482 Potsdam, Germany.ORCID 0000-0002-3895-8279

Funding

UNAM-DGAPA IT102424
6 · The paper itself

Abstract

We investigate how causal DAG learning algorithms and structural assumptions influence counterfactual decision safety. Four structure learning regimes are compared: expert-guided edge-constrained HC+BIC, unconstrained HC+BIC, MMPC+HC+BIC, and the PC-Stable algorithm. Evaluation is conducted using a leave-one-state-out protocol over a fully enumerated state-action space in a controlled offline autonomous driving setting. The environment is characterized by seven Boolean state variables and six actions, allowing us to disentangle the effects of learning strategies on counterfactual decisions. All models are implemented as probabilistic logic twin networks (

Indexed as

autonomous systemscausal discoverycounterfactual reasoningprobabilistic logic

Identifiers

PMID42187992
PMCPMC13205941

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Registered trials

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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.