Phyllis Illari
**Unfortunately, I don't have full texts of books or special issues to supply.**
My core concern is the Metaphysics, Epistemology and Methodology of Causality in the Sciences, but I have wide research interests in Philosophy of Science, particularly Philosophy of Biology and Psychology, the still-expanding Mechanisms debate, and the relatively new but vibrant Philosophy of Information. I also enjoy Plato, and can never entirely resist Ethics and Political Philosophy, which I don't have time to research, but love to teach.
I am working on an AHRC network project on Evidence and the Mechanisms Hierarchy, which looks at the problems of causal inference using mechanisms. This is in collaboration with Federica Russo and Jon Williamson at Kent, and with Brendan Clarke and Donald Gillies at UCL STS:
http://www.kent.ac.uk/secl/philosophy/jw/2012/mateh/
My core concern is the Metaphysics, Epistemology and Methodology of Causality in the Sciences, but I have wide research interests in Philosophy of Science, particularly Philosophy of Biology and Psychology, the still-expanding Mechanisms debate, and the relatively new but vibrant Philosophy of Information. I also enjoy Plato, and can never entirely resist Ethics and Political Philosophy, which I don't have time to research, but love to teach.
I am working on an AHRC network project on Evidence and the Mechanisms Hierarchy, which looks at the problems of causal inference using mechanisms. This is in collaboration with Federica Russo and Jon Williamson at Kent, and with Brendan Clarke and Donald Gillies at UCL STS:
http://www.kent.ac.uk/secl/philosophy/jw/2012/mateh/
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Books by Phyllis Illari
The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers.
The book is available for free in multiple formats and it is updated every twelve months by the team of the π Research Network: Patrick Allo, Bert Baumgaertner, Simon D'Alfonso, Penny Driscoll, Luciano Floridi, Nir Fresco, Carson Grubaugh, Phyllis Illari, Eric Kerr, Giuseppe Primiero, Federica Russo, Christoph Schulz, Mariarosaria Taddeo, Matteo Turilli, Orlin Vakarelov.
The book will be organized into four parts. The first part will contain chapters on the history of mechanistic thought. The second part will be on the nature of mechanisms and will contain chapters exploring major philosophical debates about what mechanisms are, and how they are ontologically and conceptually related to other categories of things discussed by philosophers of science and metaphysicians – for instance, causes, laws, and levels of organization. The third part covers mechanisms and philosophy of science, including issues about how scientists find, represent and explain mechanisms; in addition to general chapters on discovery, modelling and explanation, there will be chapters devoted to currently controversial topics, like the relationship between mechanisms and dynamical systems. The fourth and final part will be on disciplinary perspectives on mechanisms and will contain chapters exploring philosophical questions surrounding mechanisms studied within particular scientific fields.
Beta version published 2012, first version published 2013.
1 Phyllis McKay Illari, Federica Russo and Jon Williamson Why look at causality in the sciences? A manifesto
Part II Health sciences
2 R. Paul Thompson Causality, theories and medicine
3 Alex Broadbent Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
4 Harold Kincaid Causal modelling, mechanism, and probability in epidemiology
5 Bert Leuridan and Erik Weber The IARC and mechanistic evidence
6 Donald Gillies The Russo–Williamson thesis and the question of whether smoking causes heart disease
Part III Psychology
7 David Lagnado Causal thinking
8 Benjamin Rottman, Woo-kyoung Ahn and Christian Luhmann When and how do people reason about unobserved causes?
9 Clare R. Walsh and Steven A. Sloman Counterfactual and generative accounts of causal attribution
10 Ken Aizawa and Carl Gillett The autonomy of psychology in the age of neuroscience
11 Otto Lappi and Anna-Mari Rusanen Turing machines and causal mechanisms in cognitive science
12 Keith A. Markus Real causes and ideal manipulations: Pearl’s theory of causal inference from the point of view of psychological research methods
Part IV Social sciences
13 Daniel Little Causal mechanisms in the social realm
14 Ruth Groff Getting past Hume in the philosophy of social science
15 Michel Mouchart and Federica Russo Causal explanation: Recursive decompositions and mechanisms
16 Kevin D. Hoover Counterfactuals and causal structure
17 Damien Fennell The error term and its interpretation in structural models in econometrics
18 Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, and Abdol S. Soofi A comprehensive causality test based on the singular spectrum analysis
Part V Natural sciences
19 Tudor M. Baetu Mechanism schemas and the relationship between biological theories
20 Roberta L. Millstein Chances and causes in evolutionary biology: How many chances become one chance
21 Sahotra Sarkar Drift and the causes of evolution
22 Garrett Pendergraft In defense of a causal requirement on explanation
23 Paolo Vineis, Aneire Khan and Flavio D’Abramo Epistemological issues raised by research on climate change
24 Giovanni Boniolo, Rossella Faraldo and Antonio Saggion Explicating the notion of ‘causation’: The role of extensive quantities
25 Miklós Rédei and Balázs Gyenis Causal completeness of probability theories – Results and open problems
Part VI Computer science, probability, and statistics
26 I. Guyon, C. Aliferis, G. Cooper, A. Elisseeff, J.-P. Pellet,
P. Spirtes and A. Statnikov Causality Workbench
27 Jan Lemeire, Kris Steenhaut and Abdellah Touhafi When are graphical causal models not good models?
28 Dawn E. Holmes Why making Bayesian networks objectively Bayesian makes sense
29 Branden Fitelson and Christopher Hitchcock Probabilistic measures of causal strength
30 Kevin B. Korb, Erik P. Nyberg and Lucas Hope A new causal power theory
31 Samantha Kleinberg and Bud Mishra Multiple testing of causal hypotheses
32 Ricardo Silva Measuring latent causal structure
33 Judea Pearl The structural theory of causation
34 S. Geneletti and A.P. Dawid Defining and identifying the effect of treatment on the treated
35 Nancy Cartwright Predicting ‘It will work for us’: (Way) beyond statistics
Part VII Causality and mechanisms
36 Stathis Psillos The idea of mechanism
37 Stuart Glennan Singular and general causal relations: A mechanist perspective
38 Phyllis McKay Illari and Jon Williamson Mechanisms are real and local
39 Jim Bogen and Peter Machamer Mechanistic information and causal continuity
40 Phil Dowe The causal-process-model theory of mechanisms
41 M. Kuhlmann Mechanisms in dynamically complex systems
42 Julian Reiss Third time’s a charm: Causation, science and Wittgensteinian pluralism
Papers by Phyllis Illari
In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing – notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro – biological and psycho-social – are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.
epistemic normative constraints on what is a good explanation. I will argue for ontic constraints by drawing on Craver’s work in section 2.1, and argue for epistemic constraints by drawing on Bechtel’s work in section 2.2. Along the way, I will argue that Bechtel and Craver actually agree with this claim. I argue that we should not take either kind of constraints to be fundamental, in section 3, and close in section 4 by considering what remains at stake in making a distinction between ontic and epistemic constraints on mechanistic explanation. I suggest that we should
not concentrate on either kind of constraint, to the neglect of the other, arguing for the importance of seeing the relationship as one of integration."
The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers.
The book is available for free in multiple formats and it is updated every twelve months by the team of the π Research Network: Patrick Allo, Bert Baumgaertner, Simon D'Alfonso, Penny Driscoll, Luciano Floridi, Nir Fresco, Carson Grubaugh, Phyllis Illari, Eric Kerr, Giuseppe Primiero, Federica Russo, Christoph Schulz, Mariarosaria Taddeo, Matteo Turilli, Orlin Vakarelov.
The book will be organized into four parts. The first part will contain chapters on the history of mechanistic thought. The second part will be on the nature of mechanisms and will contain chapters exploring major philosophical debates about what mechanisms are, and how they are ontologically and conceptually related to other categories of things discussed by philosophers of science and metaphysicians – for instance, causes, laws, and levels of organization. The third part covers mechanisms and philosophy of science, including issues about how scientists find, represent and explain mechanisms; in addition to general chapters on discovery, modelling and explanation, there will be chapters devoted to currently controversial topics, like the relationship between mechanisms and dynamical systems. The fourth and final part will be on disciplinary perspectives on mechanisms and will contain chapters exploring philosophical questions surrounding mechanisms studied within particular scientific fields.
Beta version published 2012, first version published 2013.
1 Phyllis McKay Illari, Federica Russo and Jon Williamson Why look at causality in the sciences? A manifesto
Part II Health sciences
2 R. Paul Thompson Causality, theories and medicine
3 Alex Broadbent Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
4 Harold Kincaid Causal modelling, mechanism, and probability in epidemiology
5 Bert Leuridan and Erik Weber The IARC and mechanistic evidence
6 Donald Gillies The Russo–Williamson thesis and the question of whether smoking causes heart disease
Part III Psychology
7 David Lagnado Causal thinking
8 Benjamin Rottman, Woo-kyoung Ahn and Christian Luhmann When and how do people reason about unobserved causes?
9 Clare R. Walsh and Steven A. Sloman Counterfactual and generative accounts of causal attribution
10 Ken Aizawa and Carl Gillett The autonomy of psychology in the age of neuroscience
11 Otto Lappi and Anna-Mari Rusanen Turing machines and causal mechanisms in cognitive science
12 Keith A. Markus Real causes and ideal manipulations: Pearl’s theory of causal inference from the point of view of psychological research methods
Part IV Social sciences
13 Daniel Little Causal mechanisms in the social realm
14 Ruth Groff Getting past Hume in the philosophy of social science
15 Michel Mouchart and Federica Russo Causal explanation: Recursive decompositions and mechanisms
16 Kevin D. Hoover Counterfactuals and causal structure
17 Damien Fennell The error term and its interpretation in structural models in econometrics
18 Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, and Abdol S. Soofi A comprehensive causality test based on the singular spectrum analysis
Part V Natural sciences
19 Tudor M. Baetu Mechanism schemas and the relationship between biological theories
20 Roberta L. Millstein Chances and causes in evolutionary biology: How many chances become one chance
21 Sahotra Sarkar Drift and the causes of evolution
22 Garrett Pendergraft In defense of a causal requirement on explanation
23 Paolo Vineis, Aneire Khan and Flavio D’Abramo Epistemological issues raised by research on climate change
24 Giovanni Boniolo, Rossella Faraldo and Antonio Saggion Explicating the notion of ‘causation’: The role of extensive quantities
25 Miklós Rédei and Balázs Gyenis Causal completeness of probability theories – Results and open problems
Part VI Computer science, probability, and statistics
26 I. Guyon, C. Aliferis, G. Cooper, A. Elisseeff, J.-P. Pellet,
P. Spirtes and A. Statnikov Causality Workbench
27 Jan Lemeire, Kris Steenhaut and Abdellah Touhafi When are graphical causal models not good models?
28 Dawn E. Holmes Why making Bayesian networks objectively Bayesian makes sense
29 Branden Fitelson and Christopher Hitchcock Probabilistic measures of causal strength
30 Kevin B. Korb, Erik P. Nyberg and Lucas Hope A new causal power theory
31 Samantha Kleinberg and Bud Mishra Multiple testing of causal hypotheses
32 Ricardo Silva Measuring latent causal structure
33 Judea Pearl The structural theory of causation
34 S. Geneletti and A.P. Dawid Defining and identifying the effect of treatment on the treated
35 Nancy Cartwright Predicting ‘It will work for us’: (Way) beyond statistics
Part VII Causality and mechanisms
36 Stathis Psillos The idea of mechanism
37 Stuart Glennan Singular and general causal relations: A mechanist perspective
38 Phyllis McKay Illari and Jon Williamson Mechanisms are real and local
39 Jim Bogen and Peter Machamer Mechanistic information and causal continuity
40 Phil Dowe The causal-process-model theory of mechanisms
41 M. Kuhlmann Mechanisms in dynamically complex systems
42 Julian Reiss Third time’s a charm: Causation, science and Wittgensteinian pluralism
In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing – notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro – biological and psycho-social – are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.
epistemic normative constraints on what is a good explanation. I will argue for ontic constraints by drawing on Craver’s work in section 2.1, and argue for epistemic constraints by drawing on Bechtel’s work in section 2.2. Along the way, I will argue that Bechtel and Craver actually agree with this claim. I argue that we should not take either kind of constraints to be fundamental, in section 3, and close in section 4 by considering what remains at stake in making a distinction between ontic and epistemic constraints on mechanistic explanation. I suggest that we should
not concentrate on either kind of constraint, to the neglect of the other, arguing for the importance of seeing the relationship as one of integration."
While the EBM movement has been enormously successful in making explicit and critically examining one aspect of our evidential practice, i.e., evidence of correlation, we wish to extend this line of work to make explicit and critically examine a second aspect of our evidential practices: evidence of mechanisms.
finishing with examining in depth what scientific cases can do (section 7).