Summary:
‘Severe tests’ of causal claims
For many questions in the social sciences, a research design guaranteeing the validity of causal inferences is difficult to obtain. When this is the case, researchers can attempt to defend hypothesized causal relationships by seeking data that subjects their theory to repeated falsification. Karl Popper famously argued that the degree to which we have confidence in a hypothesis is not necessarily a function of the number of tests it has withstood, but rather the severity of the tests to which the hypothesis has been subjected. A test of a hypothesis with a design susceptible to hidden bias is not particularly severe or determinative. If the implication is tested in many contexts, however, with different designs that have
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Statistics & Econometrics
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‘Severe tests’ of causal claims
For many questions in the social sciences, a research design guaranteeing the validity of causal inferences is difficult to obtain. When this is the case, researchers can attempt to defend hypothesized causal relationships by seeking data that subjects their theory to repeated falsification. Karl Popper famously argued that the degree to which we have confidence in a hypothesis is not necessarily a function of the number of tests it has withstood, but rather the severity of the tests to which the hypothesis has been subjected. A test of a hypothesis with a design susceptible to hidden bias is not particularly severe or determinative. If the implication is tested in many contexts, however, with different designs that have distinct sources of bias, and the hypothesis is still not rejected, then one may have more confidence that the causal relationship is genuine. Note that repeatedly testing a hypothesis with research designs suffering from similar types of bias does not constitute a severe test, since each repetition will merely replicate the biases of the original design. In cases where randomized experiments are infeasible or credible natural experiments are unavailable, the inferential difficulties facing researchers are large. In such circumstances, only creative and severe falsification tests can make the move from correlation to causation convincing.
Daniel Hidalgo & Jasjeet Sekhon
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