Abstract
Econometric and causal modeling techniques are widely used to test causal claims both within economics and in many other areas of social sciences. My aim in this essay will be to suggest, in a rough and preliminary way, one perspective on how we should understand and evaluate these techniques. Section 2.1 reviews, very quickly, the basic apparatus of linear models. Sections 2.2 and 2.3 then discuss the idea that I take to be the key to the causal interpretability of such models—the idea of a structural or autonomous relationship. I attempt to locate this idea within a more general philosophical framework and to defend the conception of causation and explanation it embodies against various challenges. Sections 2.4 and 2.5 then explore the status of the various sorts of causal assumptions that serve as inputs to econometric techniques—assumptions about which variables to include, about causal ordering, and about the distribution of the error term. I discuss what sort of causal knowledge such input claims represent and how we can assess such claims. Section 2.6 explores similar questions concerning the output of econometric techniques—the structural models themselves.
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Woodward, J. (1995). Causation and Explanation in Econometrics. In: Little, D. (eds) On the Reliability of Economic Models. Recent Economic Thought Series, vol 42. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0643-6_2
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