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Causality is increasingly integrated into decision-making processes. Often, the goal is to optimize over causal interventions to achieve specific policy objectives. However, research into causal optimization has bifurcated into either the online optimization of interventions in causal models or the offline optimization of decision rules in causal influence diagrams. This paper introduces an approximate method for offline optimizing interventions in arbitrary hybrid Bayesian networks using observational data. The optimization problem is approached by compiling discretized Bayesian networks as binary decision diagrams, whereafter running interventional queries is very efficient. This efficiency is exploited by running heuristic optimization algorithms to optimize over the interventional queries. By running experiments on a variety of large hybrid Bayesian networks, we demonstrate the practical utility of our method and discuss policy relevance.