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Causal inference

Erkenntnis 35 (1-3):151 - 189 (1991)

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  1. Przyczyna i Wyjaśnianie: Studium Z Filozofii i Metodologii Nauk.Paweł Kawalec - 2006 - Lublin: Wydawnictwo KUL.
    Przedmowa Problematyka związana z zależnościami przyczynowymi, ich modelowaniem i odkrywa¬niem, po długiej nieobecności w filozofii i metodologii nauk, budzi współcześnie duże zainteresowanie. Wiąże się to przede wszystkim z dynamicznym rozwojem, zwłaszcza od lat 1990., technik obli¬czeniowych. Wypracowane w tym czasie sieci bayesowskie uznaje się za matematyczny język przyczynowości. Pozwalają one na daleko idącą auto¬matyzację wnioskowań, co jest także zachętą do podjęcia prób algorytmiza¬cji odkrywania przyczyn. Na potrzeby badań naukowych, które pozwalają na przeprowadzenie eksperymentu z randomizacją, standardowe metody ustalania zależności przyczynowych (...)
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  • Causal graphs and biological mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 55-86.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...)
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  • On the Properties of Conditional Independence.Wolfgang Spohn - 1994 - In Paul Humphreys (ed.), Patrick Suppes, Scientific Philosopher Vol. 1: Probability and Probabilistic Causality. Kluwer Academic Publishers.
    As the paper explains, it is crucial to epistemology in general and to the theory of causation in particular to investigate the properties of conditional independence as completely as possible. The paper summarizes the most important results concerning conditional independence with respect to two important representations of epistemic states, namely (strictly positive) probability measures and natural conditional (or disbelief or ranking) functions. It finally adds some new observations.
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  • Patterns of abduction.Gerhard Schurz - 2008 - Synthese 164 (2):201-234.
    This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...)
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  • A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: fixing the chemical factory.Nina Retzlaff & Alexander Gebharter - 2020 - Synthese 197 (4):1467-1486.
    Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that kicks in once (...)
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  • Modeling creative abduction Bayesian style.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2019 - European Journal for Philosophy of Science 9 (1):1-15.
    Schurz (Synthese 164:201–234, 2008) proposed a justification of creative abduction on the basis of the Reichenbachian principle of the common cause. In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework. We identify necessary conditions for such inferences and investigate their unificatory power. We also sketch several interesting applications of modeling creative abduction Bayesian style. In particular, we discuss use-novel predictions, confirmation, and the (...)
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  • Uncovering constitutive relevance relations in mechanisms.Alexander Gebharter - 2017 - Philosophical Studies 174 (11):2645-2666.
    In this paper I argue that constitutive relevance relations in mechanisms behave like a special kind of causal relation in at least one important respect: Under suitable circumstances constitutive relevance relations produce the Markov factorization. Based on this observation one may wonder whether standard methods for causal discovery could be fruitfully applied to uncover constitutive relevance relations. This paper is intended as a first step into this new area of philosophical research. I investigate to what extent the PC algorithm, originally (...)
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  • The Ontic Account of Scientific Explanation.Carl F. Craver - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 27-52.
    According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...)
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  • Causal Markov, robustness and the quantum correlations.Mauricio Suárez & Iñaki San Pedro - 2010 - In Probabilities, Causes and Propensities in Physics. New York: Springer. pp. 173–193.
    It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal inference generally, (...)
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  • Epr Robustness and the Causal Markov Condition.Mauricio Suárez & Iñaki San Pedro - 2007 - Centre of Philosophy of Natural and Social Science.
    It is still a matter of controversy whether the Principle of the Common Cause can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal inference generally, which (...)
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  • The conserved quantity theory of causation and chance raising.Phil Dowe - 1999 - Philosophy of Science 66 (3):501.
    In this paper I offer an 'integrating account' of singular causation, where the term 'integrating' refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The 'process' intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of 'misconnections', where an event which serves to prevent another fails to do so on a particular (...)
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  • Combining causal Bayes nets and cellular automata: A hybrid modelling approach to mechanisms.Alexander Gebharter & Daniel Koch - 2021 - British Journal for the Philosophy of Science 72 (3):839-864.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...)
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  • Solving the Flagpole Problem.Alexander Gebharter - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):63-67.
    In this paper I demonstrate that the causal structure of flagpole-like systems can be determined by application of causal graph theory. Additional information about the ordering of events in time or about how parameters of the systems of interest can be manipulated is not needed.
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