The encapsulation efficiency (%EE), used here as a consistently reported operational proxy for encapsulation performance in literature-derived datasets, in niosomal drug delivery systems has been analyzed using predictive modeling. However, these methods are hardly able to support intervention-oriented reasoning adequately. Therefore, the present study adopts a developed causal discovery framework on a data set of formulation from the literature to reveal assumption-aware causal structures connecting physicochemical drug properties, formulation composition, process conditions, and %EE. The input variables were selected based on consistent reporting across literature sources, physicochemical relevance to niosomal systems, and unambiguous availability in the extracted data, rather than outcome-driven criteria. From 116 observational samples of thin-film hydration only, causal graphs were generated through the combined use of constraint-based (PC Algorithm), score-based (Greedy Equivalence Search (GES)), and functional (Linear Non-Gaussian Acyclic Model (LiNGAM)) models. In common with the other methods, %EE was identified as a structurally downstream outcome. At the same time, the intrinsic drug properties (especially lipophilicity) were found to be upstream constraints rather than intervention levers. A notable systematic difference between the importance of features predictive of the model and the causal effect estimates was observed, underscoring that predictive relevance does not imply causal influence. Instead of dictating formulation rules, the framework allows for generating hypotheses, counterfactual exploration, and structured experimental planning under explicit causal assumptions.