arXiv subjects

Mark Louie F. Ramos

Publications and source records attributed to Mark Louie F. Ramos.

5 recordsLinked to original sources

arXiv2026

Quantifying Precision in Measurement of Individualized, Evidence-Based Learning Across Sessions in Applied Behavior Analysis Therapy

Current performance criteria used in Applied Behavior Analysis (ABA) are often based on whether a learner meets a predetermined benchmark e.g., 8 out of 10 correct responses across several sessions. Although these criteria help practitioners make instructional decisions, they provide limited information about how likely it is that the learner has truly acquired the skill. In this paper, we introduce a Bayesian approach that combines practitioners' prior expectations with the learner's ongoing performance data to estimate the learner's future performance level. Rather than providing only a pass or fail decision based on the criteria, we add an element of precision around that criteria to make decisions more nuanced to the testing conditions used. We also developed an R Shiny application for Measurement of Individualized, Evidence-Based Learning (MIEBL) that allows practitioners to apply this method in practice, supporting more informed instructional decisions and providing insight into the strengths and limitations of commonly used mastery criteria.

stat.AP↗

arXiv2026

Asymptotic emergence of statistically supported false causal interpretation under unmeasured confounding

Unmeasured confounding is widely recognized as a limitation of observational causal inference, but its implications for data-driven causal discovery are often understated. We provide an asymptotic characterization of this limitation. When the true causal variable is unobserved but correlated proxy variables are available, increasing sample size while searching for increasingly stable, data-driven causal structures will make associations involving observable proxies become increasingly certain. This result highlights a fundamental distinction between reducing statistical uncertainty and recovering causal mechanisms: more data can improve certainty about genuine relationships within the observed variable space while providing no guarantee of identifying the underlying causal structure.

stat.ME↗

arXiv2026

Embarrassingly Causal: Causal Use of Associational Data in Magic The Gathering Drafts

Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the criterion of embarrassingly causal scenarios, where the existence of an exposure outcome relationship is so uncontroversial that the assumptions needed to include the corresponding causal edge in a DAG can be reasonably made. Using the case of Magic The Gathering booster draft decisions and gameplay outcomes, we show how purely observational data from 17Lands are widely and effectively used to guide draft choices despite substantial confounding, selection effects, and post treatment conditioning. We argue that the embarrassingly causal quality is a sufficient condition for justifying the construction of causal estimands and the collection of observational data to estimate them. Correspondingly, we provide guidance on evaluating observational causal inference assumptions for authors, reviewers, and readers.

stat.ME↗

arXiv2026

Is it Cake or is it AI? A Systematic Review of Human Uncertainty in Distinguishing Generative Artificial Intelligence Content

This systematic review synthesized empirical evidence on human ability to distinguish generative artificial intelligence content from human produced content across text, image, and voice modalities. A structured search of Scopus identified 22,541 records from 2025 to 2026, of which 1200 were screened and 30 studies were included. Across these studies, human detection accuracy varied widely but generally clustered around chance performance. Overall, the literature shows that humans are generally unreliable detectors of gen AI content, raising broader questions about whether the ability to tell should matter for how we evaluate or trust content.

stat.AP↗

arXiv2026

Rethinking how randomness is taught in K-12 statistics subjects

The effective teaching and learning of statistics persist as a challenge in K-12 education and has clear impacts in developing competence and confidence of students in entering STEM fields especially in today's digital age of data science. In this paper, it is proposed that the current pedagogy around teaching the concept of randomness, a core element of statistics, is incomplete. It is argued that expanding on the observer-relative nature of randomness will address commonly existing ambiguities on what is and what is not random, help in explaining more complex statistical concepts such as hypothesis testing, and impart greater awareness on the practical value of the practice of statistics in data science.

math.HO↗