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Semmelweis's discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have ...
The manipulationist account of causation provides a conceptual analysis of cause-effect relationships in terms of hypothetical experiments. It also explains why and how experiments are used for the em...
Inference to the best explanation has at times appeared almost indistinguishable from a rule that recommends simply that we should infer the hypothesis which is most plausible given available evidence...
Abstract: It is often assumed without argument that fictionalism in the philosophy of science contradicts scientific realism. This paper is a critical analysis of this assumption. The kind of fictiona...
A probabilistic logic of induction is unable to separate cleanly neutral support from disfavoring evidence (or ignorance from disbelief). Thus, the use of probabilistic representations may introduce s...
Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., j...
Going back at least to Duhem, there is a tradition of thinking that crucial experiments are impossible in science. I analyse Duhem’s arguments and show that they are based on the excessively strong as...
Did God create life? Or did life arise via naturalistic processes, along the lines of random mutation and natural selection as suggested by Darwin? Intelligent design proponents attempt to use William...
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory change, and proposes a framework for dealing with such changes. It first presents a scheme for generating...
This talk proposes a solution to the "paradox of thought experiments": how seemingly new empirical insights are derived by “thought experiments” which are entirely conducted inside one’s head, so to s...
The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it a...
Inference to the Best Explanation and Bayesianism have both been proposed as descriptions of the way that people make inferences. This paper argues that one result from cognitive psychology, the "femi...
Many algorithms proposed in the machine learning community for inferring causality from data are grounded on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition...
There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first stat...
This paper analyzes Deborah Mayo's error-statistical (ES) account of scientific evidence in order to clarify the kinds of "material postulates" it requires and to explain how those assumptions functio...

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