How can we interpret results from complex machine learning algorithms? How can we mitigate the risks associated with using such models for policy decisions? This project addresses fundamental challenges in deriving valid, reliable, and interpretable causal conclusions from complex data using modern machine learning tools. As machine learning becomes increasingly integral to disciplines such as medicine, economics, education, and the social sciences, the demand for causal insight --- beyond predictive accuracy --- has become more pressing. Yet many machine learning algorithms function as “black boxes”, offering limited transparency and lacking rigorous frameworks for replicability and uncertainty quantification. This project aims to establish a theoretical foundation for causal learning that makes outputs from machine learning explainable, statistically sound, and actionable in real-world decision-making. The work is complemented by educational and outreach activities that promote understanding of causal reasoning among students and the broader public. Planned efforts include public lectures, collaborations with K–12 educators, and integration of research findings into university curricula. Collaborative partnerships with institutions such as Microsoft, Eli Lilly, and the Fred Hutchinson Cancer Center will help translate methodological advances into impactful scientific and societal applications. Technically, the project advances causal learning through three interrelated