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The design of effective therapeutic interventions can be formulated as Markov decision processes in the framework of context-sensitive probabilistic Boolean networks, assuming that the states are measurable. The full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile. In practice, however, the network context may not be measurable; hence, adaptive methods are required to devise effective therapeutic strategies. To this end, Open-Loop Feedback Intervention that acts as if a form of the certainty equivalence principle would have been held is proposed, and its performance is evaluated.