Explanation causal

All biological functions in an organism are interdependent and internally regulated and since their occurrence is context-dependent, they cannot be understood in isolation. Functional explanations are therefore more appropriate for understanding complex systems exhibiting many coupled interactions than are causal explanations which focus on a single factor. 

Hafner, H Riecher-Rossler, A., An der Heiden, W., Maurer, K., Fatkenheuer, and Loffler, W. 1993. Generating and testing a causal explanation of the gender difference in age at first onset of schizophrenia. Psychological Medicine 23 925—940. 

Finally, causal explanations must be distinguished from predictions. Sometimes we can explain without being able to predict, and sometimes predict without being able to explain. True, in many cases one and the same theory will enable us to do both, but I believe that in the social sciences this is the exception rather than the rule. 

These two processes constitute a straightforward, causal explanation of advanced and retarded forces and potentials. Net effect at P is obtained by vector addition. 

Registering on a screen, an individual spot, as seen in Scully et al. model, epitomizes (in the standard approach) an individual measurement (Cf. Ref [11]). If one focuses only on the spots, individual results then look purely random without any possibility of detailed causal explanation [17]. From the perspective developed here, the spots will pattern the quantum state one sets up to measure. Energy in the form of quanta is required to imprint the measuring device. The result is an ordered picture of a quantum state emerging from the spots aggregate in laboratory (real) space. The event also includes information on the probing apparatus with associated noise. 

Macquer and Baume s classification of affinities thus introduced a typology of chemical operations, allowing them to expand the discourse of affinity as a general explanatory scheme. Through their classificatory efforts, the notion of affinity became an integral part of chemical explanation. Chemists could simply invoke different kinds of affinity to explain multitudes of chemical operations without further regressing into complicated causal explanations or ontologies. 

More spedfically, I shall argue that Marx himself offers an alternative framework that allows fora much more predseand fertile analysis. On this view, social science explanations are seen as three-tiered. First, there is a causal explanation of mental states, such as desires and beliefs (1.3.1). Next, there is intentional explanation of individual action in terms of the underlying beliefs and desires (1.2). Finally, there is causa) explanation of aggregate phenomena in terms of the individual actions that go into them. The last form is the specifically Marxist contribution to the methodology of the social sciences. I discuss it fust as a particular mode of causal analysis (1.3.2) and then again as a particular form of dialectical reasoning (1.5.3). 

In the threC tiered scheme of explanation suggested above, two varieties of causal analysis were involved. First, there are the causal explanations of preferences and other mental states, such as beliefs, emotions etc. I shall refer to this as explanation in terms of sub-intentional causality. Next, there are the causal explanations of aggregate social phenomena as the resultant outcome of many individual actions. This 1 refer to as supra-intentional causality. Metaphorically, the causal mechanisms involved in both cases can be said to operate "behind the back" of the individuals concerned. True, Marx uses that phrase only to refer to supra-intentional causality - the production of unintended consequences that thwart our efforts and subvert our aims. Yet the expression is equally apt as a characterization of the psychic causality that, unbeknown to the agent, shapes his beliefs and desires. Although Marx is best known for his study of supra-intentional causality, one aim of the present work is to argue that he was also a pioneer in the study of preference formation and - especially - belief formation. 

Barnes, B., On the Causal Explanation ot Scientific Judgment , Social Science Information, 19 (1980), pp. 685-95. 

When we have two (or more) treatments , we will usually wish to carry out a hypothesis test. This is making a decision about whether it is plausible that the difference (if any) between the treatments is real . In Table 7.2, we might wish to consider whether treatment B really does give a higher rate of side-effects than treatment A. To address this question, the alternative non-causal explanations for the apparent difference must be considered. They are (a) bias in allocation or group membership, (b) assessment or measurement bias, and (c) chance. 

Suppose a study of two treatments for a disease had resulted in the data shown in Table 7.10. Under what circumstances would it be possible to conclude that the difference in the percentages of success in the two treatment groups has been caused by the superiority of treatment A over treatment B The simple principle we use is that we can conclude that the difference reflects causation if it is possible to exclude any other possible explanation for the difference. The non-causal explanations can be categorised as in the following sections. 

Since scientific theories attempt to present causal explanations for physical phenomena, naturally they must comport with observations of the world. Even the most elegant theory is not considered helpful if it does not provide an ability to explain or predict physical events. A theory that is able to make an accurate prediction of some instance of a physical phenomenon, or one that is able to provide a foundation for the explanation of the phenomenon, is a theory that has some apparent value for gaining a greater understanding of the world. As such, theories and hypotheses must be in accord with the reality of the world, and a scientific investigation must ultimately, if not immediately, seek to compare the implications of the theory or hypothesis with empirical data. 

Vigorous controversies notwithstanding, I must begin by acknowledging that the roles of constraint and contingency in evolutionary processes are, of course, not mutually exclusive. In fact they are mutually necessary (Carroll, 2001). Nevertheless, a significant amount of both the scientific and interdisciplinary literature on these issues has been characterized by an unnecessary dichotomization of causal explanations and a regrettable polarization of rhetoric. There are two reasons for this, both of which I hope at least to avoid, if not to redress. 

The difference between the epistemic model of explanation and the ontic one becomes clear at this point Cause... because, as Philip Kitcher elegantly puts it, is the slogan of the epistemic school. Explanations frequently refer to causes simply because they explain much, but there is no conditio sine qua non (1989). If it turned out that other non-causal regularities were better explanatory vehicles the causal explanation would be dropped. The adherent of the ontic model of explanation, on the other hand, maintains that any valid explanation has to cite causes of the explanandum because explanation owes its only possible sense to a thorough assumption about the ontic constitution of our world. Those who want to capture the validity of explanations in their dependency on causes have to give an answer to the question of what causality consists in. Here, I sketch the two major accounts of causality. 

Hence, in the light of our both accounts of causality, the molecular dynamics model represents causal processes or chains of events. But is the derivation of a molecule s structure by a molecular dynamics simulation a causal explanation Here the answer is no. The molecular dynamics model alone is not used to explain a causal story elucidating the time evolution of the molecule s conformations. It is used to find the equilibrium conformation situation that comes about a theoretically infinite time interval. The calculation of a molecule s trajectory is only the first step in deriving any observable structural property of this molecule. After a molecular dynamics search we have to screen its trajectory for the energetic minima. We apply the Boltzmann distribution principle to infer the most probable conformation of this molecule.17 It is not a causal principle at work here. This principle is derived from thermodynamics, and hence is statistical. For example, to derive the expression for the Boltzmann distribution, one crucial step is to determine the number of possible realizations there are for each specific distribution of items over a number of energy levels. There is no existing explanation for something like the molecular partition function for a system in thermodynamic equilibrium solely by means of causal processes or causal stories based on considerations on closest possible worlds. 

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