The primary criterion of adequacy of a probabilistic causal analysis is that the causal variable should render the simultaneous phenomenological data conditionally independent. The intuition back of this idea is that the common cause of the phenomena should factor out the observed correlations. So we label the principle the common cause criterion . If we find that the barometric pressure and temperature are both dropping at the same time, we do not think of one as the cause of the other but look for a common dynamical cause within the physical theory of meteorology. If we find fever and headaches positively correlated, we look for a common disease as the source and do not consider one the cause of the other. But we do not want to suggest that satisfaction of this criterion is the end of the search for causes or probabilistic explanations. It does represent a significant and important milestone in any particular investigation. Under another banner the search for common causes in quantum mechanics is the search for hidden variables. A hidden variable that satisfies the common cause criterion provides a satisfactory explanation “in classical terms” of the quantum phenomenon. Much of the earlier discussion of hidden variables in quantum mechanics has centered around the search for deterministic underlying processes, but for some time now the literature has also been concerned with the existence of probabilistic hidden variables. It is a striking and important fact that even probabilistic hidden variables do not always exist when certain intuitive criteria are imposed.
The primary criterion of adequacy of a probabilistic causal analysis is that the causal variable should render the simultaneous phenomenological data conditionally independent. The intuition back of this idea is that the common cause of the phenomena should factor out the observed correlations. So we label the principle the common cause criterion . If we find that the barometric pressure and temperature are both dropping at the same time, we do not think of one as the cause of the other but look for a common dynamical cause within the physical theory of meteorology. If we find fever and headaches positively correlated, we look for a common disease as the source and do not consider one the cause of the other. But we do not want to suggest that satisfaction of this criterion is the end of the search for causes or probabilistic explanations. It does represent a significant and important milestone in any particular investigation. Under another banner the search for common causes in quantum mechanics is the search for hidden variables. A hidden variable that satisfies the common cause criterion provides a satisfactory explanation “in classical terms” of the quantum phenomenon. Much of the earlier discussion of hidden variables in quantum mechanics has centered around the search for deterministic underlying processes, but for some time now the literature has also been concerned with the existence of probabilistic hidden variables. It is a striking and important fact that even probabilistic hidden variables do not always exist when certain intuitive criteria are imposed.
Hardy presented a famous argument that shows that there exist no hidden variables in quantum mechanics. His argument is called Hardy's paradox. The commentaries and discussions on Hardy's paradox have been ordinarily presented for deterministic hidden variables, and it has not been discussed whether a similar paradox will occurs in probabilistic hidden variables. So, first, I will show that Hardy's argument can also be applied to probabilistic hidden variables. After that, I discuss the common cause of a correlation, which has been actively discussed in the philosophy of science in recent years. That is because requiring the common cause of a quantum correlation is mathematically equivalent to introducing a probabilistic hidden variable to explain the correlation. Some researchers argued that in the non-existence proof of hidden variables, the existence of a common common-cause to multiple correlations is tacitly assumed, however that is unreasonably strong. According to their idea, it is sufficient if there exists a respective common-cause of each correlation. However, it will be revealed that at least as far as Hardy's paradox is concerned, merely weakening the common common-cause requirement to the respective common-cause requirement does not help to construct a local model.
The common cause principle says that every correlation is either due to a direct causal effect linking the correlated entities or is brought about by a third factor, a so-called common cause. The principle is of central importance in the philosophy of science, especially in causal explanation, causal modeling and in the foundations of quantum physics. Written for philosophers of science, physicists and statisticians, this book contributes to the debate over the validity of the common cause principle, by proving results that bring to the surface the nature of explanation by common causes. It provides a technical and mathematically rigorous examination of the notion of common cause, providing an analysis not only in terms of classical probability measure spaces, which is typical in the available literature, but in quantum probability theory as well. The authors provide numerous open problems to further the debate and encourage future research in this field.
Reichenbach's principle asserts that if two observed variables are found to be correlated, then there should be a causal explanation of these correlations.Furthermore, if the explanation is in terms of a common cause, then the conditional probability distribution over the variables given the complete common cause should factorize.The principle is generalized by the formalism of causal models, in which the causal relationships among variables constrain the form of their joint probability distribution.In the quantum case, however, the observed correlations in Bell experiments cannot be explained in the manner Reichenbach's principle would seem to demand.Motivated by this, we introduce a quantum counterpart to the principle.We demonstrate that under the assumption that quantum dynamics is fundamentally unitary, if a quantum channel with input A and outputs B and C is compatible with A being a complete common cause of B and C, then it must factorize in a particular way.Finally, we show how to generalize our quantum version of Reichenbach's principle to a formalism for quantum causal models and provide examples of how the formalism works.
The aim of this paper is to give a review of the known results and open problems concerning causal completeness (closedness) of classical and non-classical (quantum) probability spaces. Causal closedness of a probabilistic theory means that the theory is causally rich enough to be able to explain causally all the correlations it predicts. It is natural to ask whether probabilistic theories are causally closed if one assumes that Reichenbach’s Common Cause Principle holds: this principle states that if two events A and B are probabilistically correlated then either the correlation is due to a causal interaction between A and B, or, if A and B are causally independent, Rind(A,B), then there exists a third event C, a so-called common cause that explains the correlation by being related to A and B probabilistically in a specific way (see Definition 1). Causal closedness of a probabilistic theory is intended to express that the theory complies with the Common Cause Principle; accordingly, and more precisely, a probability theory is defined to be causally closed with respect to a causal independence relation
No correlation without causation. This is, in its most compact and general formulation, the essence of what has become Reichenbach's Common Cause Principle . More explicitly, the Common Cause Principle says that every correlation is either due to a direct causal effect linking the correlated entities, or is brought about by a third factor, a so-called Reichenbachian common cause that stands in a well-defined probabilistic relation to the correlated events, a relation that explains the correlation in the sense of entailing it. The Common Cause Principle is a nontrivial metaphysical claim about the causal structure of the World and entails that all correlations can (hence, should) be explained causally either by pointing at a causal connection between the correlated entities or by displaying a common cause of the correlation. Thus, the Common Cause Principle licenses one to infer causal connections from probabilistic relations; at the same time the principle does not address whether the causal connection holds between the correlated entities or between the common cause and the elements in the correlation. While the technically explicit notion of common cause of a probabilistic correlation within the framework of classical Kolmogorovian probability theory is due to Reichenbach (1956), the Common Cause Principle was articulated explicitly only later, especially in the works by W. Salmon (see the “Notes and bibliographic remarks” to Chapter 2). The chief aim of this book is to investigate the Common Cause Principle; in particular, the problem of to what extent the Common Cause Principle can explain probabilistic correlations.
Causal closedness and common cause closedness Assuming that Reichenbach's Common Cause Principle is valid, one is led to the question of whether our probabilistic theories predicting probabilistic correlations can be causally rich enough to also contain the causes of all the correlations they predict. The aim of this chapter is to formulate precisely and investigate this question. According to the Common Cause Principle, causal richness of a theory T would manifest in T 's being causally closed (complete) in the sense of being capable of explaining the correlations by containing a common cause of every correlation between causally independent events A, B . This feature of a theory is formulated in the next two definitions of causal closedness. In both definitions ( X, S, p )is a probability space and R ind is a two-place causal independence relation that is assumed to have been defined between elements of S . We treat the relation R ind as a variable in the problem of causal closedness; hence, at this point we leave open what properties R ind should have to be acceptable as a causal independence relation – later we will return to the issue of how to specify it. Definition 4.1 The probability space ( X, S, p ) is called causally closed with respect to R ind , if for every correlation Corr p ( A, B ) ＞ 0 with A ϵ S and B ϵ S such that R ind ( A, B ) holds, there exists a common cause C of some type in S .