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This paper examines the relations between the causal models of Pearl and the settable systems framework recently introduced by White and Chalak. We pay particular attention to the suitability of these two approaches for analyzing the behavior of optimizing, interacting agents, the central concern of economics. We show that Pearlks causal model is nested in the settable system framework, and we describe a number of ways in which Pearlks causal models are not well suited to the study of interacting, optimizing agents. In contrast, settable systems have been explicitly designed to facilitate this study, accommodating systems of agents that not only optimize and interact, but that may learn from experience. We illustrate using examples from microeconomics, option pricing, game theory, and recursive estimation. Among the features of settable systems that distinguish it from Pearlks causal models and that help deliver its capabilities are its countable rather than finite structure, its explicit use of attributes, and the introduction of partitions and partition% specific response functions.