Personalized treatment selection and planning rely on accurate hypotheses about the causes of clients' problems and the factors that hinder achievement of clients' recovery goals. Yet, accurately identifying these causal factors can be challenging because of practical considerations (eg, time pressure) and vulnerabilities in human cognition (eg, reasoning biases), particularly when clients' symptoms are nonspecific. Causal discovery analyses — an emerging class of machine-learning methods — provide a data-driven, potentially more accurate method of individualized case conceptualization and treatment selection/evaluation. They can also be used to augment client insight, increase awareness to motivate willingness to change, and are a compelling visual aid for discussions of treatment rationale. This article provides an accessible introduction to these methods, with the goal of enabling clinicians to be informed partners when teaming with data scientists around measurement-based care and precision medicine. [Psychiatr Ann. 2024;54(4):e119–e124.]