It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.

I argue that causation is a contrastive relation: c-rather-than-C* causes e-rather-than-E*, where C* and E* are contrast classes associated respectively with actual events c and e. I explain why this is an improvement on the traditional binary view, and develop a detailed definition. It turns out that causation is only well defined in 'uniform' cases, where either all or none of the members of C* are related appropriately to members of E*.

This article begins by surveying existing work on scientific models, with an eye to the specific case of economics. It reviews four accounts in particular—the satisfaction-of-assumptions account, the capacities account, the credible-worlds account, and the partial-structures account. It tells the detailed story of the 1994 Federal Communications Commission (FCC) spectrum auction in the United States, highlighting the crucial role of experiment as well as theory. In the light of this case study, this article presents its own open-formula account of economic models. It then turns to the issue of economic progress. Finally, it concludes that empirical progress in economic theory might not be discussed here, or at least that the success of the spectrum auction provides no warrant for doing so. Rather, progress is better seen as more akin to the worthy but piecemeal variety typical of engineering.

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