by On the limits of formal methods in causal inference Our problem is … with the temptation to think that by stating some of our assumptions more clearly, we have successfully formalized the entire inferential process … Science may indeed seek objectivity, and for this reason a deductive method for causal inference is indeed highly desirable. But this does not mean that it is possible: we cannot have one just because we decide we need one. Causal conclusions do not follow deductively from data without a strong set of auxiliary assumptions, and … these assumptions are themselves not deductive consequences of the data. A formal method may indeed be extremely helpful, provided that its significance is not misunderstood and its dependence on supporting
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Lars Pålsson Syll considers the following as important: Theory of Science & Methodology
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On the limits of formal methods in causal inference
Our problem is … with the temptation to think that by stating some of our assumptions more clearly, we have successfully formalized the entire inferential process … Science may indeed seek objectivity, and for this reason a deductive method for causal inference is indeed highly desirable. But this does not mean that it is possible: we cannot have one just because we decide we need one. Causal conclusions do not follow deductively from data without a strong set of auxiliary assumptions, and … these assumptions are themselves not deductive consequences of the data. A formal method may indeed be extremely helpful, provided that its significance is not misunderstood and its dependence on supporting assumptions not forgotten …
If it is claimed that causal inference has been formalized and it is not explained that the formalism, powerful as it may be, is only as good as the assumptions that support it, then causal conclusions will look surer (‘more objective’) than they really are …
Estimations either of counterfactual contrasts or of interventions are interesting and important, but are often local effects in a particular time, place and population. And even these are not pure empirical findings, but are heavily theory-laden. They are not read or calculated from data, but inferred from it, and the inference depends upon a huge network of background hypotheses and scientific knowledge … Thus, causality is not a statistical concept whose presence or absence can be determined by statistical analysis of a set of data. It is a theoretical concept, even when invoked in quantitative estimates for particular populations. As with any scientific theoretical finding, we infer causal conclusions (including estimations of causal effect) as the result of an inductive inference, considering all the available evidence.