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This volume contends that Evidential Pluralism—an account of the
epistemology of causation, which maintains that in order to
establish a causal claim one needs to establish the existence of a
correlation and the existence of a mechanism—can be fruitfully
applied to the social sciences. Through case studies in sociology,
economics, political science and law, it advances new philosophical
foundations for causal enquiry in the social sciences. The book
provides an account of how to establish and evaluate causal claims
and it offers a new way of thinking about evidence-based policy,
basic social science research and mixed methods research. As such,
it will appeal to scholars with interests in social science
research and methodology, the philosophy of science and
evidence-based policy.
While probabilistic logics in principle might be applied to solve a
range of problems, in practice they are rarely applied - perhaps
because they seem disparate, complicated, and computationally
intractable. This programmatic book argues that several approaches
to probabilistic logic fit into a simple unifying framework in
which logically complex evidence is used to associate probability
intervals or probabilities with sentences. Specifically, Part I
shows that there is a natural way to present a question posed in
probabilistic logic, and that various inferential procedures
provide semantics for that question, while Part II shows that there
is the potential to develop computationally feasible methods to
mesh with this framework. The book is intended for researchers in
philosophy, logic, computer science and statistics. A familiarity
with mathematical concepts and notation is presumed, but no
advanced knowledge of logic or probability theory is required.
This book is open access under a CC BY license. This book is the
first to develop explicit methods for evaluating evidence of
mechanisms in the field of medicine. It explains why it can be
important to make this evidence explicit, and describes how to take
such evidence into account in the evidence appraisal process. In
addition, it develops procedures for seeking evidence of
mechanisms, for evaluating evidence of mechanisms, and for
combining this evaluation with evidence of association in order to
yield an overall assessment of effectiveness. Evidence-based
medicine seeks to achieve improved health outcomes by making
evidence explicit and by developing explicit methods for evaluating
it. To date, evidence-based medicine has largely focused on
evidence of association produced by clinical studies. As such, it
has tended to overlook evidence of pathophysiological mechanisms
and evidence of the mechanisms of action of interventions. The book
offers a useful guide for all those whose work involves evaluating
evidence in the health sciences, including those who need to
determine the effectiveness of health interventions and those who
need to ascertain the effects of environmental exposures.
While probabilistic logics in principle might be applied to solve a
range of problems, in practice they are rarely applied - perhaps
because they seem disparate, complicated, and computationally
intractable. This programmatic book argues that several approaches
to probabilistic logic fit into a simple unifying framework in
which logically complex evidence is used to associate probability
intervals or probabilities with sentences. Specifically, Part I
shows that there is a natural way to present a question posed in
probabilistic logic, and that various inferential procedures
provide semantics for that question, while Part II shows that there
is the potential to develop computationally feasible methods to
mesh with this framework. The book is intended for researchers in
philosophy, logic, computer science and statistics. A familiarity
with mathematical concepts and notation is presumed, but no
advanced knowledge of logic or probability theory is required.
An accessible guide for those facing the study of Logic for the
first time, this book covers key thinkers, terms and texts. "The
Key Terms in Philosophy" series offers clear, concise and
accessible introductions to the central topics in philosophy. Each
book offers a comprehensive overview of the key terms, concepts,
thinkers and major works in the history of a key area of
philosophy. Ideal for first-year students starting out in
philosophy, the series will serve as the ideal companion to study
of this fascinating subject. "Key Terms in Logic" offers the ideal
introduction to this core area in the study of philosophy,
providing detailed summaries of the important concepts in the study
of logic and the application of logic to the rest of philosophy. A
brief introduction provides context and background, while the
following chapters offer detailed definitions of key terms and
concepts, introductions to the work of key thinkers and lists of
key texts. Designed specifically to meet the needs of students and
assuming no prior knowledge of the subject, this is the ideal
reference tool for those coming to Logic for the first time. "The
Key Terms" series offers undergraduate students clear, concise and
accessible introductions to core topics. Each book includes a
comprehensive overview of the key terms, concepts, thinkers and
texts in the area covered and ends with a guide to further
resources.
Causal inference is perhaps the most important form of reasoning in
the sciences. A panoply of disciplines, ranging from epidemiology
to biology, from econometrics to physics, make use of probability
and statistics in order to infer causal relationships. However, the
very foundations of causal inference are up in the air; it is by no
means clear which methods of causal inference should be used, nor
why they work when they do. This book brings philosophers and
scientists together to tackle these important questions. The papers
in this volume shed light on the relationship between causality and
probability and the application of these concepts within the
sciences. With its interdisciplinary perspective and its careful
analysis, "Causality and Probability in the Sciences" heralds the
transition of causal inference from an art to a science.
Logic is a field studied mainly by researchers and students of
philosophy, mathematics and computing. Inductive logic seeks to
determine the extent to which the premisses of an argument entail
its conclusion, aiming to provide a theory of how one should reason
in the face of uncertainty. It has applications to decision making
and artificial intelligence, as well as how scientists should
reason when not in possession of the full facts. In this book, Jon
Williamson embarks on a quest to find a general, reasonable,
applicable inductive logic (GRAIL), all the while examining why
pioneers such as Ludwig Wittgenstein and Rudolf Carnap did not
entirely succeed in this task. Along the way he presents a general
framework for the field, and reaches a new inductive logic, which
builds upon recent developments in Bayesian epistemology (a theory
about how strongly one should believe the various propositions that
one can express). The book explores this logic in detail, discusses
some key criticisms, and considers how it might be justified. Is
this truly the GRAIL? Although the book presents new research, this
material is well suited to being delivered as a series of lectures
to students of philosophy, mathematics, or computing and doubles as
an introduction to the field of inductive logic
There is a need for integrated thinking about causality,
probability and mechanisms in scientific methodology. Causality and
probability are long-established central concepts in the sciences,
with a corresponding philosophical literature examining their
problems. On the other hand, the philosophical literature examining
mechanisms is not long-established, and there is no clear idea of
how mechanisms relate to causality and probability. But we need
some idea if we are to understand causal inference in the sciences:
a panoply of disciplines, ranging from epidemiology to biology,
from econometrics to physics, routinely make use of probability,
statistics, theory and mechanisms to infer causal relationships.
These disciplines have developed very different methods, where
causality and probability often seem to have different
understandings, and where the mechanisms involved often look very
different. This variegated situation raises the question of whether
the different sciences are really using different concepts, or
whether progress in understanding the tools of causal inference in
some sciences can lead to progress in other sciences. The book
tackles these questions as well as others concerning the use of
causality in the sciences.
This is an accessible guide for those facing the study of Logic for
the first time, this book covers key thinkers, terms and texts.
"The Key Terms in Philosophy" series offers clear, concise and
accessible introductions to the central topics in philosophy. Each
book offers a comprehensive overview of the key terms, concepts,
thinkers and major works in the history of a key area of
philosophy. Ideal for first-year students starting out in
philosophy, the series will serve as the ideal companion to study
of this fascinating subject. "Key Terms in Logic" offers the ideal
introduction to this core area in the study of philosophy,
providing detailed summaries of the important concepts in the study
of logic and the application of logic to the rest of philosophy. A
brief introduction provides context and background, while the
following chapters offer detailed definitions of key terms and
concepts, introductions to the work of key thinkers and lists of
key texts. Designed specifically to meet the needs of students and
assuming no prior knowledge of the subject, this is the ideal
reference tool for those coming to Logic for the first time. "The
Key Terms" series offers undergraduate students clear, concise and
accessible introductions to core topics. Each book includes a
comprehensive overview of the key terms, concepts, thinkers and
texts in the area covered and ends with a guide to further
resources.
How strongly should you believe the various propositions that you
can express?
That is the key question facing Bayesian epistemology. Subjective
Bayesians hold that it is largely (though not entirely) up to the
agent as to which degrees of belief to adopt. Objective Bayesians,
on the other hand, maintain that appropriate degrees of belief are
largely (though not entirely) determined by the agent's evidence.
This book states and defends a version of objective Bayesian
epistemology. According to this version, objective Bayesianism is
characterized by three norms:
DT Probability - degrees of belief should be probabilities
DT Calibration - they should be calibrated with evidence
DT Equivocation - they should otherwise equivocate between basic
outcomes
Objective Bayesianism has been challenged on a number of different
fronts. For example, some claim it is poorly motivated, or fails to
handle qualitative evidence, or yields counter-intuitive degrees of
belief after updating, or suffers from a failure to learn from
experience. It has also been accused of being computationally
intractable, susceptible to paradox, language dependent, and of not
being objective enough.
Especially suitable for graduates or researchers in philosophy of
science, foundations of statistics and artificial intelligence, the
book argues that these criticisms can be met and that objective
Bayesianism is a promising theory with an exciting agenda for
further research.
Bayesian nets are widely used in artificial intelligence as a
calculus for casual reasoning, enabling machines to make
predictions, perform diagnoses, take decisions and even to discover
casual relationships. But many philosophers have criticized and
ultimately rejected the central assumption on which such work is
based-the causal Markov Condition. So should Bayesian nets be
abandoned? What explains their success in artificial intelligence?
This book argues that the Causal Markov Condition holds as a
default rule: it often holds but may need to be repealed in the
face of counter examples. Thus, Bayesian nets are the right tool to
use by default but naively applying them can lead to problems. The
book develops a systematic account of causal reasoning and shows
how Bayesian nets can be coherently employed to automate the
reasoning processes of an artificial agent. The resulting framework
for causal reasoning involves not only new algorithms, but also new
conceptual foundations. Probability and causality are treated as
mental notions - part of an agent's belief state. Yet probability
and causality are also objective - different agents with the same
background knowledge ought to adopt the same or similar
probabilistic and causal beliefs. This book, aimed at researchers
and graduate students in computer science, mathematics and
philosophy, provides a general introduction to these philosophical
views as well as exposition of the computational techniques that
they motivate.
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