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Scientific Explanation and the Causal Structure of the World
The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view (a version of the epistemic conception) is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception.
Professor Salmon's theory furnishes a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation (that explanations are arguments) to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts.
Professor Salmon's theory furnishes a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation (that explanations are arguments) to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts.
- GenresPhilosophyScience
321 pages, Paperback
First published November 1, 1984
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December 26, 2021Ultimately the book was more focused on examples from physics than I had hoped. The book's pitch for a philosophy of science that "accords with the pervasively statistical character of contemporary science" was appealing to me, a social scientist. But had I done my research a little more, I would have known more about Salmon's focus on natural sciences. I don't know enough about the different sides of the nuanced debates Salmon was engaged in.
One simple thing the book helped me think about is the formulation of research questions. In Salmon's view, one should be able to formulate a "why" question if one is doing real research. I tried to apply this to my own work, and found it surprisingly hard. Most of my research is answering questions of the form "how big is effect X" or "what is the effect of policy Y."
Before, I had thought about this in terms of forward or reverse-causal questions. We are well equipped, with our modern econometric and statistical causal inference tools, to answer questions where we know the cause. A straightforward case is instrumental variables. If one has a valid instrument, one can find the effect caused by that instrument. But how to you answer the reverse question? How do you determine what caused an outcome? That is more challenging. There can be many causes of different types. A "why" question would seem to require answering this challenging question, though.
But one can also think of "why" questions as the substantive theory portion of the scientific process. The "how" question (in the sense of "how big") establishes a fact, and the "why" question uses substantive theory to try to explain the fact. In my old conception, how and why were at odds, but this newer conception has how and why working together.
One simple thing the book helped me think about is the formulation of research questions. In Salmon's view, one should be able to formulate a "why" question if one is doing real research. I tried to apply this to my own work, and found it surprisingly hard. Most of my research is answering questions of the form "how big is effect X" or "what is the effect of policy Y."
Before, I had thought about this in terms of forward or reverse-causal questions. We are well equipped, with our modern econometric and statistical causal inference tools, to answer questions where we know the cause. A straightforward case is instrumental variables. If one has a valid instrument, one can find the effect caused by that instrument. But how to you answer the reverse question? How do you determine what caused an outcome? That is more challenging. There can be many causes of different types. A "why" question would seem to require answering this challenging question, though.
But one can also think of "why" questions as the substantive theory portion of the scientific process. The "how" question (in the sense of "how big") establishes a fact, and the "why" question uses substantive theory to try to explain the fact. In my old conception, how and why were at odds, but this newer conception has how and why working together.
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