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  1. Manipulationism, Ceteris Paribus Laws, and the Bugbear of Background Knowledge.Robert Kowalenko - 2017 - International Studies in the Philosophy of Science 31 (3):261-283.
    According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibili-ty. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generali-sations, and these are not well adapted to encoding such knowledge, (...)
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  • A Difference-Making Account of Causation.Wolfgang Pietsch - unknown
    A difference-making account of causality is proposed that is based on a counterfactual definition, but differs from traditional counterfactual approaches to causation in a number of crucial respects: it introduces a notion of causal irrelevance; it evaluates the truth-value of counterfactual statements in terms of difference-making; it renders causal statements background-dependent. On the basis of the fundamental notions 'causal relevance' and 'causal irrelevance', further causal concepts are defined including causal factors, alternative causes, and importantly inus-conditions. Problems and advantages of the (...)
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  • Let’s Take the Bias Out of Econometrics.Duo Qin - 2019 - Journal of Economic Methodology 26 (2):81-98.
    ABSTRACTThis study exposes the cognitive flaws of ‘endogeneity bias’. It examines how conceptualisation of the bias has evolved to embrace all major econometric problems, despite extensive lack of hard evidence. It reveals the crux of the bias – a priori rejection of causal variables as conditionally valid ones, and of the bias correction by consistent estimators – modification of those variables by non-uniquely and non-causally generated regressors. It traces the flaws to misconceptions about error terms and estimation consistency. It highlights (...)
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