The causal frame problem is an epistemological puzzle about how the mind is able to disregard seemingly irrelevant causal knowledge, and focus on those factors that promise to be useful in making an inference or coming to a decision. Taking a subject’s causal knowledge to be (implicitly) represented in terms of directed graphical models, the causal frame problem can be construed as the question of how to determine a reasonable “submodel” of one’s “full model” of the world, so as to optimize the balance between accuracy in prediction on the one hand, and computational costs on the other. We propose a framework for addressing this problem, and provide several illustrative examples based on HMMs and Bayes nets. We also show that our framework can account for some of the recent empirical phenomena associated with alternative neglect.