The conserved quantity theory of causation and chance raising

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 occasion and yet the two events are linked by causal processes. The chance raising intuition, according to which causes raise the chance of their effects, easily accounts for misconnections but faces the problem of chance lowering causes, a problem easily accounted for by the process approach. The integrating program attempts to provide an analysis of singular causation by synthesising the two insights, so as to solve both problems. In this paper I show that extant versions of the integrating program due to Eells, Lewis, and Menzies fail to account for the chance-lowering counterexample. I offer a new diagnosis of the chance lowering case, and use that as a basis for an integrating account of causation which does solve both cases. In doing so, I accept various assumptions of the integrating program, in particular that there are no other problems with these two approaches. As an example of the process account, I focus on the recent CQ theory of Wesley Salmon (1997).

Monte Carlo evidence on cointegration and causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.

A defense of backwards in time causation models in quantum mechanics

This paper offers a defense of backwards in time causation models in quantum mechanics. Particular attention is given to Cramer's transactional account, which is shown to have the threefold virtue of solving the Bell problem, explaining the complex conjugate aspect of the quantum mechanical formalism, and explaining various quantum mysteries such as Schrodinger's cat. The question is therefore asked, why has this model not received more attention from physicists and philosophers? One objection given by physicists in assessing Cramer's theory was that it is not testable. This paper seeks to answer this concern by utilizing an argument that backwards causation models entail a fork theory of causal direction. From the backwards causation model together with the fork theory one can deduce empirical predictions. Finally, the objection that this strategy is questionable because of its appeal to philosophy is deflected.

Direction of Causation Modeling Between Cross-Sectional Measures of Parenting and Psychological Distress in Female Twins

Under certain conditions, cross-sectional analysis of cross-twin intertrait correlations can provide important information about the direction of causation (DOC) between two variables. A community-based sample of Australian female twins aged 18 to 45 years was mailed an extensive Health and Lifestyle Questionnaire (HLQ) that covered a wide range of personality and behavioral measures. Included were self-report measures of recent psychological distress and perceived childhood environment (PBI). Factor analysis of the PBI yielded three interpretable dimensions: Coldness, Overprotection, and Autonomy. Univariate analysis revealed that parental Overprotection and Autonomy were best explained by additive genetic, shared, and nonshared environmental effects (ACE), whereas the best-fitting model for PBI Coldness and the three measures of psychological distress (Depression, Phobic Anxiety, and Somatic Distress) included only additive genetic and nonshared environmental effects (AE). A common pathway model best explained the covariation between (1) the three PBI dimensions and (2) the three measures of psychological distress. DOC modeling between latent constructs of parenting and psychological distress revealed that a model which specified recollected parental behavior as the cause of psychological distress provided a better fit than a model which specified psychological distress as the cause of recollected parental behavior. Power analyses and limitations of the findings are discussed.

Nonreductive physicalism and mental causation

In this work I articulate...

Associations, causation and model in psychiatry

This paper discusses models, associations and causation in psychiatry. The different types of association (linear, positive, negative, exponential, partial, U shaped relationship, hidden and spurious) between variables involved in mental disorders are presented as well as the use of multiple regression analysis to disentangle interrelatedness amongst multiple variables. A useful model should have internal consistency, external validity and predictive power; be dynamic in order to accommodate new sound knowledge; and should fit facts rather than they other way around. It is argued that whilst models are theoretical constructs they also convey a style of reasoning and can change clinical practice. Cause and effect are complex phenomena in that the same cause can yield different effects. Conversely, the same effect can have a different range of causes. In mental disorders and human behaviour there is always a chain of events initiated by the indirect and remote cause; followed by intermediate causes; and finally the direct and more immediate cause. Causes of mental disorders are grouped as those: (i) which are necessary and sufficient; (ii) which are necessary but not sufficient; and (iii) which are neither necessary nor sufficient, but when present increase the risk for mental disorders.

Mental Causation : Investigating the mind's powers in a natural world

4.6 Summary and Conclusion In this chapter, we have first tried to make precise the distinctions between the concepts of parthood and coincidence and the concepts of causation and causal influence. These distinc-tions had never been made entirely explicit in the debate on mental causation before, despite the fact that they constantly figure in its background. Section 4.2 then demonstrated that the at-tained definitions are both compatible with all the solutions elaborated in chapters 2 and 3 and that they are even of great help in clarifying both what precisely the mentioned accounts are claiming respectively and what their mutual connections are. In sections 4.3. and 4.4, we have then tried to explore two possible solutions to the problem of mental causation that, at least in these particular versions, have not been explicitly defended in the literature. These solutions we dubbed "overdeteiminationism lite" and "plural determinism". We found the accounts both to bear impressive explanatory capabilities and to be vulnerable to far fewer problems than is commonly supposed. We also found out that they have many corresponding aspects and that their theoretical costs stand in a relation of a relative mutual balance. Our final discussion in section 4.5 revealed, however, that overdetenninationism lite should probably be considered the more successful theory. The fact that it needs to endorse the existence of two kinds of causation, although not unproblematic itself, did not appear as a commitment as strong as that of an ontological hierarchy that extends over all time, which at least the broad version of plural determinism was forced to make.

Tackling Societal Challenges Related to Ageing and Transport Transition: An Introduction to Philosophical Principles of Causation Adapted to the Biopsychosocial Model

In geriatrics, driving cessation is addressed within the biopsychosocial model. This has broadened the scope of practitioners, not only in terms of assessing fitness to drive, but also by helping to maintain social engagements and provide support for transport transition. Causes can be addressed at different levels by adapting medication, improving physical health, modifying behaviour, adapting lifestyle, or bringing changes to the environment. This transdisciplinary approach requires an understanding of how different disciplines are linked to each other. This article reviews the philosophical principles of causality between fields and provides a framework for understanding causality within the biopsychosocial model. Understanding interlevel constraints should help practitioners overcome their differences, and favor transversal approaches to driving cessation.

Regularity and counterfactuality in Hume's treatment of causation

Of the several theories of causation current in our days, Hume is said to be the inspiration of two of the most influential and accepted: the regularity theory, first clearly formulated by Thomas Brown in 1822, and the counterfactual theory, proposed by David Lewis in 1973. After a brief outline of the comparative merits and difficulties of these two views, I proceed to examine whether Hume's own treatment of causation actually corresponds to any of them. I will show that his first definition of cause, coupled with his rules by which to judge about causes and effects, contains elements that, properly developed, allow us to address successfully some traditional difficulties of the regularity view of causation, without resorting to the conceptual resources employed in the counterfactual approach. Therefore, we can properly classify Hume as an advocate of the conception of causation as regularity, noting however that his primary goal in his research and definitions of the concept was to provide not so much an analysis of causation as such, but of causation as we apprehend it, in the form of our ability to make causal inferences and refine them to reach the more sophisticated causal reasonings that are required in the theoretical and practical issues of life.