599,654 research outputs found
How do medical researchers make causal inferences?
Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snowās inference about the cause of cholera
Invisible injuries: concussive effects and international humanitarian law
The concussive effects of weapons used on the modern battlefield can cause Traumatic Brain Injury (TBI). Indeed, TBI has been termed the "signature wound" of the ongoing conflicts in Iraq and Afghanistan. To date, the injury has not been taken into account by armed forces in their application of international humanitarian law norms regarding attacks that affect civilians. Of particular note in this regard are the rule of proportionality and the requirement to take precautions in attack. This article opens the discussion about this recently discovered consequence of warfare for the civilian population. It examines the state of the science regarding TBI and queries whether the understanding of such injuries has reached the point at which commanders in the field are obligated to begin considering, as a matter of humanitarian law, the risk of causing TBI to civilians when they attack enemy forces. It concludes with a practical assessment of how they might do so
Punitive Damages, Social Norms, and Economic Analysis
Cooter offers an economic analysis of punitive damages, keeping in mind the role of social norms. Liability for compensatory damages provides efficient incentives for self-monitoring
Causality and Association: The Statistical and Legal Approaches
This paper discusses different needs and approaches to establishing
``causation'' that are relevant in legal cases involving statistical input
based on epidemiological (or more generally observational or population-based)
information. We distinguish between three versions of ``cause'': the first
involves negligence in providing or allowing exposure, the second involves
``cause'' as it is shown through a scientifically proved increased risk of an
outcome from the exposure in a population, and the third considers ``cause'' as
it might apply to an individual plaintiff based on the first two. The
population-oriented ``cause'' is that commonly addressed by statisticians, and
we propose a variation on the Bradford Hill approach to testing such causality
in an observational framework, and discuss how such a systematic series of
tests might be considered in a legal context. We review some current legal
approaches to using probabilistic statements, and link these with the
scientific methodology as developed here. In particular, we provide an approach
both to the idea of individual outcomes being caused on a balance of
probabilities, and to the idea of material contribution to such outcomes.
Statistical terminology and legal usage of terms such as ``proof on the balance
of probabilities'' or ``causation'' can easily become confused, largely due to
similar language describing dissimilar concepts; we conclude, however, that a
careful analysis can identify and separate those areas in which a legal
decision alone is required and those areas in which scientific approaches are
useful.Comment: Published in at http://dx.doi.org/10.1214/07-STS234 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Symbolic Partial-Order Execution for Testing Multi-Threaded Programs
We describe a technique for systematic testing of multi-threaded programs. We
combine Quasi-Optimal Partial-Order Reduction, a state-of-the-art technique
that tackles path explosion due to interleaving non-determinism, with symbolic
execution to handle data non-determinism. Our technique iteratively and
exhaustively finds all executions of the program. It represents program
executions using partial orders and finds the next execution using an
underlying unfolding semantics. We avoid the exploration of redundant program
traces using cutoff events. We implemented our technique as an extension of
KLEE and evaluated it on a set of large multi-threaded C programs. Our
experiments found several previously undiscovered bugs and undefined behaviors
in memcached and GNU sort, showing that the new method is capable of finding
bugs in industrial-size benchmarks.Comment: Extended version of a paper presented at CAV'2
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