Abstract
A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: outcomes that would have been observed had the world developed differently, e.g. if the patient had received a different treatment. By definition, we can never observe such quantities, nor can we assess empirically the validity of any modelling assumptions we may make about them, even though our conclusions may be sensitive to these assumptions. Here we argue that, for making inference about the likely effects of applied causes, counterfactual arguments are unnecessary and potentially misleading. An alternative approach, based on Bayesian decision analysis, is presented. Properties of counterfactuals are relevant to inference about the likely causes of observed effects, but then close attention to what can and cannot be supported empirically is needed to qualify the conclusions drawn, and unambiguous inferences will generally only be possible when they can be based on an essentially deterministic theory.
Keywords
- Causal Inference
- Counterfactual Analysis
- Untestable Assumption
- Average Causal Effect
- Counterfactual Approach
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1999 Springer-Verlag Berlin Heidelberg
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Dawid, A.P. (1999). Who Needs Counterfactuals?. In: Gammerman, A. (eds) Causal Models and Intelligent Data Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58648-4_3
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DOI: https://doi.org/10.1007/978-3-642-58648-4_3
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