Limits of scientific explanation (I) (original) (raw)
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2019
Science neither aims at having the monopoly over the truth about the world nor establishing a dogmatic knowledge. Natural light of experience is held by empiricists to be the reliable source of human knowledge. Inductive logic has been a leading tool of empirical experiments in justifying and confirming scientific theories with evidence. Science cannot reach where it has reached without inductive logic. Inductive logic has, therefore, played an important role in making science what it is today. Inductive logic helps science to justify its theories not form convictional opinions of scientists but from factual propositions. However, inductive logic has been problematic in the sense that its logic of justification led philosophers of science to demarcation, the distinction of episteme from doxa. At present, some philosophers of science and scientists attempt to justify why science carries out a reliable knowledge. Some have argued for structuralism and realism of scientific theories rather than believing in the course of miracles and others for their historicity. Both views are explanatories of how science works and progresses. This essay recalls the arguments for structures of scientific theories and their historicity. First, the essay analyses the controversy between Rudolf Carnap and Karl Popper on how the problem of inductive logic in confirming scientific theories can be solved. In so doing, the essay refers to empirical probabilities as well as the limits calculus. Second, the essay merges frequentist and Bayesian approaches to determine how scientific theories are to be confirmed or refuted. Third, the use of a new form of Bayesian Theorem will show how mathematical and logical structures respond to some of the important questions that arise from the historical and realistic views about scientific theories. The essay argues for epistemic objectivity behind inductive probability, the key issue of the controversy in question, and proves that the truth about the world is symmetric. Keywords: Science; Induction; Probability; Demarcation; Deduction; Frequentism; Bayesianism.
The Red Herring of Probability Raising
Theorists of probabilistic causality viewed causation as probability raising relative to particular contexts. In contrast, more recent graphical theories do not specify whether a cause raises or lowers the probability of its effect as part of the causal representation, but enable one to use one’s prior causal knowledge to infer such quantitative facts from the joint probability distribution. While this difference between the accounts may seem minor, here I argue that the focus on probability raising hindered theorists of probabilistic causality from grasping the relationship between causation and probability. The idea that causation is linked to a specific quantitative probabilistic relationship led these theorists to conflate issues about confounding with those related to effect heterogeneity, and to engage in debates that in retrospect do not appear to reflect substantive philosophical differences. In contrast, while graphical models provide a way of representing the qualitative causal relations among a set of variables and determining whether the dependence relationship can be in principle inferred from the probability distribution given one’s causal assumptions, the particular quantitative relationship is not specified as part of the model, but inferred from the distribution. This contrast is key to understanding the philosophical differences between how the earlier and later literature understood the relationship between causation and probability.
The Bayesian model of probabilistic inference and the probability of theories 1
Andrés Rivadulla: Éxito, razón y cambio en física, Madrid: Ed. Trotta, 2004
In these pages I offer my solution to the problem of inductive probability of theories. Against the existing expectations in certain areas of the current philosophy of science, I argue that Bayes’s Theorem does not constitute an appropriate tool to assess the probability of theories and that we would do well to banish the question about how likely a certain scientific theory is to be true, or to what extent one theory is more likely true than another. Although I agree with Popper that inductive probability is impossible, I disagree with him in the way Sir Karl presents his argument, as I have showed elsewhere, so my proof is completely different. The argument I present in this paper is based on applying Bayes’s Theorem to specific situations that show its inefficiency both in the case of whether a hypothesis becomes all the more likely true the greater the empirical evidence that supports it, as whether the probability calculus allows to identify a given hypothesis from a set of hypotheses incompatible with each other as the most likely true.
Probability-Lowering Causes and the Connotations of Causation
Ideas y Valores 151: 43-55, 2013
A common objection to probabilistic theories of causation is that there are prima facie causes that lower the probability of their effects. Among the many replies to this objection, little attention has been given to Mellor’s (1995) indirect strategy to deny that probability-lowering factors are bona fide causes. According to Mellor, such factors do not satisfy the evidential, explanatory, and instrumental connotations of causation. The paper argues that the evidential connotation only entails an epistemically relativized form of causal attribution, not causation itself, and that there are clear cases of explanation and instrumental reasoning that must appeal to negatively relevant factors. In the end, it suggests a more liberal interpretation of causation that restores its connotations.
2020
Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that pa...