The study presents a method for selecting cost-effective experiments that enhance the identification of causal effects from observational data, addressing the challenge of partial identifiability. By introducing efficient pruning techniques, the research significantly reduces the number of experiments needed, demonstrating practical applications on real-world data related to health outcomes.