Efficient causal discovery is essential for constructing reliable causal graphs that provide actionable insights in domains where randomized experiments are infeasible. This study introduces DKC, a novel causal discovery algorithm that utilizes both observational data and prior knowledge to enable reliable learning of causal graphs that supports decision-making in complex domains such as healthcare. Traditional causal discovery methods often rely exclusively on observational data, which reduces their effectiveness when datasets are noisy, limited in size, or involve intricate causal relationships. Moreover, existing approaches seldom incorporate prior knowledge in a flexible manner, limiting their applicability in real-world scenarios. DKC addresses these challenges by efficiently incorporating causal priors into the discovery process through a tailored scoring criterion that supports both hard and soft constraints. The framework operates in three stages: (i) estimation of a topological ordering of variables, (ii) ranking candidate edges according to likelihood, and (iii) performing a constrained causal search using the proposed score to balance model fit, complexity, and prior knowledge. We establish theoretical guarantees demonstrating that the score is statistically consistent, converging to the true causal structure as sample size grows. Extensive experiments on synthetic datasets of varying scales, as well as real-world healthcare data, confirm that DKC outperforms state-of-the-art baselines in terms of structural accuracy and robustness. By harmonizing data-driven insights with prior knowledge, DKC provides a trustworthy foundation for causal inference across diverse fields. Its application to a clinical problem highlights its potential to guide critical decision-making, while its general framework ensures broad utility in any domains requiring reliable, knowledge-informed causal reasoning.
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