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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.