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  1. Causal inference in biomedical research.Tudor M. Baetu - 2020 - Biology and Philosophy 35 (4):1-19.
    Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...)
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  2. Pain in psychology, biology and medicine: Some implications for pain eliminativism.Tudor M. Baetu - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 82:101292.
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  3. On pain experience, multidisciplinary integration and the level-laden conception of science.Tudor M. Baetu - 2019 - Synthese 196 (8):3231-3250.
    Multidisciplinary models aggregating ‘lower-level’ biological and ‘higher-level’ psychological and social determinants of a phenomenon raise a puzzle. How is the interaction between the physical, the psychological and the social conceptualized and explained? Using biopsychosocial models of pain as an illustration, I argue that these models are in fact level-neutral compilations of empirical findings about correlated and causally relevant factors, and as such they neither assume, nor entail a conceptual or ontological stratification into levels of description, explanation or reality. If inter-level (...)
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  4. Mechanism schemas and the relationship between biological theories.Tudor M. Baetu - 2011 - In Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences. Oxford University Press.
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  5. How interventionist accounts of causation work in experimental practice and why there is no need to worry about supervenience.Tudor M. Baetu - 2021 - Synthese 199 (1-2):4601-4620.
    It has been argued that supervenience generates unavoidable confounding problems for interventionist accounts of causation, to the point that we must choose between interventionism and supervenience. According to one solution, the dilemma can be defused by excluding non-causal determinants of an outcome as potential confounders. I argue that this solution undermines the methodological validity of causal tests. Moreover, we don’t have to choose between interventionism and supervenience in the first place. Some confounding problems are effectively circumvented by experimental designs routinely (...)
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