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Materials informatics has achieved rapid progress in predicting composition–structure–property relationships, enabling accelerated screening, surrogate modeling, and exploration of high-dimensional design spaces. However, much of this success remains structurally grounded in correlational learning rather than in explanatory, transportable, or intervention-relevant forms of understanding. This conceptual manuscript argues that correlation-centric models, while often sufficient for ranking candidates under training-like conditions, are epistemically underpowered for high-stakes materials decisions such as processing optimization, microstructural control, deployment certification, and failure-sensitive design, where actions must remain defensible under distribution shift, partial observability, and changing constraints. In such settings, predictive accuracy alone does not establish decision legitimacy: a model may be correct for reasons that do not remain stable under deliberate intervention, confounding, or selection effects, thereby producing actionable recommendations without causal warrant. Motivated by recent developments in structural causal models, causal discovery, counterfactual inference, and invariant representation learning, this paper advances a theory-first reframing: materials AI should be treated as an epistemic instrument whose outputs must be qualified by the causal status they can legitimately support. We propose a novel framework—the Causal Warrant Ladder (CWL)—that classifies materials-model outputs into five ascending levels of causal legitimacy: associative regularities, transportable relations, mechanistic constraints, interventional guidance, and counterfactual design claims. CWL is paired with a Causal-Readiness Map, which specifies the minimal conceptual conditions required for upward movement on the ladder, including identifiability assumptions, invariance structure, intervention semantics, and decision stakes. By separating predictive competence from causal legitimacy, this roadmap provides a disciplined conceptual pathway beyond “black-box correlation” toward materials reasoning that supports robust and responsible design action.

Materials informatics has emerged as a central paradigm in contemporary materials science, leveraging machine learning and data-driven modeling to accelerate materials discovery, optimization, and deployment. Despite substantial advances in predictive accuracy, most existing approaches remain fundamentally correlational, limiting their reliability under distribution shifts, experimental interventions, and real-world deployment scenarios. This reliance on correlation constrains scientific interpretability and undermines the capacity of AI systems to function as genuine instruments of materials reasoning. Causality offers a principled framework for overcoming these limitations by explicitly modeling cause-and-effect relationships among composition, processing, structure, and properties. This narrative review synthesizes conceptual progress in integrating causal inference into materials informatics, examining foundational causal frameworks, advances in causal discovery, and hybrid causal–machine learning approaches, and emerging applications across materials domains such as nanocatalysis, ferroelectrics, and electrochemical energy storage. We critically analyze persistent challenges—including data scarcity, assumption violations, limited external validity, and computational and epistemic constraints—that currently hinder widespread adoption. Drawing exclusively on peer-reviewed literature published, the review emphasizes thematic and epistemic developments rather than algorithmic prescriptions. We argue that causality represents a structural shift in how AI systems contribute to materials science: from correlational predictors to intervention-aware, mechanism-aligned reasoning tools. By articulating future directions centered on hybrid modeling, domain-knowledge integration, and interdisciplinary collaboration, this review positions causality as a necessary foundation for robust, generalizable, and scientifically legitimate materials informatics.

This review systematically examines the conceptual treatment of causality within materials informatics literature published between 2017 and 2024, drawing exclusively on a curated set of 26 studies identified through targeted and broadened searches across Web of Science, Scopus, arXiv, and specialized databases using terms such as “causal inference,” “causality materials informatics,” “structural causal model,” “directed acyclic graph,” “intervention materials design,” and “counterfactual materials prediction,” with inclusion criteria focused on relevance to materials AI while allowing broader engineering and general causal frameworks where they intersect with materials problems. The analysis reveals a pronounced dominance of correlation-based approaches in materials artificial intelligence, where predictive models achieve impressive statistical fits for structure-property relationships yet seldom progress to robust causal claims, as evidenced by the majority of surveyed works prioritizing accuracy metrics over interventional or counterfactual reasoning. Key causal concepts and frameworks, primarily drawn from Pearl’s foundational hierarchy of association, intervention, and counterfactuals as well as structural causal models and directed acyclic graphs, are introduced and contrasted with their limited adoption in the field. Causal methods that have been applied, albeit sparingly, to materials informatics—ranging from data-driven causal discovery to Bayesian causal modeling—are surveyed alongside their strengths and context-specific limitations. Persistent challenges, including the rarity of randomized interventions in experimental materials workflows and the confounding effects inherent in high-dimensional observational datasets, are highlighted as barriers that leave substantial gaps in the literature. Ultimately, this review offers targeted recommendations for authors, reviewers, and the broader community to integrate causal reasoning more explicitly, thereby moving materials informatics from correlational prediction toward actionable intervention and counterfactual understanding essential for autonomous materials design.

Microstructure–property mapping remains a foundational challenge in materials science and engineering. Microstructural features such as grain size, phase distribution, and defect density are known to govern macroscopic properties including strength, conductivity, and toughness. Yet the relationship between microstructure and property is rarely a simple direct link. Processing history, trace impurities, measurement conditions, and other hidden factors act as confounders that simultaneously influence both microstructure and observed properties. Standard machine learning approaches, which dominate current computational materials engineering, learn statistical correlations from historical datasets rather than causal mechanisms. As a result, these models frequently fail when applied to new microstructures produced under different processing conditions or in novel material systems. Unobserved confounding introduces systematic bias that cannot be corrected through conventional statistical adjustment. Historical processing data are often incomplete or proprietary, impurities fall below detection limits, and measurement artifacts vary across laboratories. Consequently, correlation-based predictions break down under distribution shifts, limiting their utility for materials design and extrapolation. This paper presents a conceptual causal inference framework tailored to microstructure–property mapping in the presence of unobserved confounders. The framework rests on four core components: causal graph specification to formalize directed relationships among microstructure features (M), properties (Y), observed confounders, and unobserved confounders; confounder identification to separate what can and cannot be measured; instrumental variable estimation to recover unbiased causal effects; and counterfactual reasoning to answer interventional questions essential for design. By integrating these elements, the framework enables predictions that remain robust when processing histories change and supports counterfactual inquiries of the form “what would the property be if the microstructure were altered while holding all else fixed?” Recent advances in causal analysis of microstructural data  and domain-adapted representation learning  illustrate the practical relevance of such an approach. The framework also connects to causal discovery techniques suitable for high-dimensional microstructural images and time-series processing records. Ultimately, it shifts computational materials engineering from correlational prediction toward causal understanding, offering a pathway to reliable extrapolation, improved transfer across material classes, and more effective rational design of next-generation materials with targeted performance.