Roughly a quarter of a century ago, the author proposed "Observable System Theory" (OST) as a foundational theory of systems theory. OST describes both statistics and quantum mechanics within a single framework of state–observable–measured value, governed by two explicitly formulated principles: the Born principle and the causal principle. This paper examines Bayesian networks (BNs) from the standpoint of OST, with particular attention to the following fundamental question: what, ultimately, do DAG calculations compute? The answer proposed here is that DAG calculations ultimately compute the probabilistic causality of measured values. Accordingly, OST admits two distinct notions of causality. The first is the causal evolution of states—equivalently, the causal evolution of observables in the Heisenberg picture—governed by OST's causal principle. The second is the probabilistic dependence structure among measured values, represented by a directed acyclic graph (DAG) together with local conditional probability distributions. The main result of this paper is the following fundamental theorem: for every finite Bayesian network specified by a DAG and local conditional probability distributions, there exists a corresponding measurement in OST whose outcome distribution coincides exactly with the joint probability distribution generated by the Bayesian network. We also aim to dispel the notion that theories involving multiple, distinct kinds of causal relation—such as Aristotle's doctrine of the four causes—are inherently untenable. Keywords: Observable System Theory; Bayesian network; directed acyclic graph; measurement; measured value; measurement probability.
Roughly a quarter of a century ago, the author proposed "Observable System Theory" (OST) as a foundational theory of systems theory. OST describes both statistics and quantum mechanics within a single framework of state–observable–measured value, governed by two explicitly formulated principles: the Born principle and the causal principle. This paper examines Bayesian networks (BNs) from the standpoint of OST, with particular attention to the following fundamental question: what, ultimately, do DAG calculations compute? The answer proposed here is that DAG calculations ultimately compute the probabilistic causality of measured values. Accordingly, OST admits two distinct notions of causality. The first is the causal evolution of states—equivalently, the causal evolution of observables in the Heisenberg picture—governed by OST's causal principle. The second is the probabilistic dependence structure among measured values, represented by a directed acyclic graph (DAG) together with local conditional probability distributions. The main result of this paper is the following fundamental theorem: for every finite Bayesian network specified by a DAG and local conditional probability distributions, there exists a corresponding measurement in OST whose outcome distribution coincides exactly with the joint probability distribution generated by the Bayesian network. We also aim to dispel the notion that theories involving multiple, distinct kinds of causal relation—such as Aristotle's doctrine of the four causes—are inherently untenable. Keywords: Observable System Theory; Bayesian network; directed acyclic graph; measurement; measured value; measurement probability.
I have proposed OST (Observable System Theory) as a foundational theory of system theory.3 This theory adopts two principles, namely, the Born principle and the Causal principle, and furthermore explicitly uses the one-shot measurement principle as a rule of use. OST is one of the most fundamental scientific theories, alongside mathematics and physics, and it can provide a unified framework for discussing quantum mechanics (information), statistics (system theory), practical logic, epistemology (philosophy), statistical mechanics, and so on.In this paper, I clarify that Fisherian statistics, Bayesian statistics, and quantum mechanics share a common mathematical foundation, namely OST. In statistics, an OST-based understanding of the Kalman filter is presented. Moreover, the so-called “interpretation problem” of quantum mechanics after measurement is unnecessary, and the “Wigner’s friend paradox” is resolved within OST. In addition, the FR paradox (= Frauchiger–Renner paradox) is understood as a paradox arising from the violation of the one-shot measurement principle, and thus it suggests the validity of OST. Through these discussions, I claim the following two points:(i) OST-based statistics will, in the future, become the standard understanding of statistics.(ii) OST-based quantum mechanics is the final form of von Neumann-style quantum mechanics
I have proposed OST (Observable System Theory) as a foundational theory of system theory. Roughly speaking, OST is a theory that unifies statistics and quantum mechanics. In comparison with ordinary statistics, its main advantage is that it possesses an explicit axiomatic structure. In this sense, I have claimed the following two points:(i): OST will, in the future, become the standard understanding of statistics.(ii): OST-based quantum mechanics is the final form of von Neumann-style quantum mechanics.Statistical mechanics, however, involves microscopic states, probability distributions, Gibbs states, coarse graining, time evolution, thermodynamic limits, and so on. For this reason, a complete axiomatization of statistical mechanics is generally regarded as difficult. In the present paper, we propose a different strategy: the system-theoretical part of statistical mechanics is formulated by means of the axiomatic structure of OST, while the remaining parts are treated in the conventional way, withoutforcing them into a single axiomatic system.The aim of this paper is to understand statistical mechanics in the language of OST. In other words, the parts of statistical mechanics in which statistics has traditionally been used are here reformulated by means of OST. Thus, statistical mechanics is examined from the viewpoint of the three-term structure: state, observable, measured value. Although this is only a partial axiomatization, it nevertheless allows us to make the following claim: (iii): OST-based statistical mechanics will, in the future, become the standard understanding of statistical mechanics.This should be regarded as a corollary of (i) above. At least from the foundational viewpoint of how statistical mechanics should be read as a descriptive specification, the author believes that the OST-based understanding of statistical mechanics is more natural than the conventional one.
This paper presents a framework for quantum causal modeling based on the interpretation of causality as a relation between an observer's probability assignments to hypothetical or counterfactual experiments. The framework is based on the principle of ``causal sufficiency'': that it should be possible to make inferences about interventions using only the probabilities from a single ``reference experiment'' plus causal structure in the form of a directed acyclic graph. This leads to several interesting results: we find that quantum measurements deserve a special status distinct from interventions, and that a special rule is needed for making inferences about what would happen if they are not performed (``unmeasurements''). One natural candidate for this rule is found to be an equation of importance to the quantum Bayesianism interpretation of quantum mechanics. We find that the causal structure of quantum systems must have a ``layered'' structure, and that the model can naturally be made symmetric under reversal of the causal arrows.
It is known that the classical framework of causal models is not general enough to allow for causal reasoning about quantum systems. While the framework has been generalized in a variety of different ways to the quantum case, much of this work leaves open whether causal concepts are fundamental to quantum theory, or only find application at an emergent level of classical devices and measurement outcomes. Here, we present a framework of quantum causal models, with causal relations defined in terms intrinsic to quantum theory, and the central object of study being the quantum process itself. Following Allen et al., Phys. Rev. X 7, 031021 (2017), the approach defines quantum causal relations in terms of unitary evolution, in a way analogous to an approach to classical causal models that assumes underlying determinism and situates causal relations in functional dependences between variables. We show that any unitary quantum circuit has a causal structure corresponding to a directed acyclic graph, and that when marginalising over local noise sources, the resulting quantum process satisfies a Markov condition with respect to the graph. We also prove a converse to this statement. We introduce an intrinsically quantum notion that plays a role analogous to the conditional independence of classical variables, and (generalizing a central theorem of the classical framework) show that d-separation is sound and complete for it in the quantum case. We present generalizations of the three rules of the classical do-calculus, in each case relating a property of the causal structure to a formal property of the quantum process, and to an operational statement concerning the outcomes of interventions. In addition, we introduce and derive similar results for classical split-node causal models, which are more closely analogous to quantum causal models than the classical causal models that are usually studied.
We study quantum analogues of Bayesian networks on a directed acyclic graph (DAG), distinguishing two constructions for positive definite density operators on finite-dimensional tensor-product Hilbert spaces. The intrinsic construction starts from a joint state and its conditional-independence properties. The extrinsic construction assembles a state sequentially from prescribed local quantum kernels, following an ordering compatible with the arrows of the DAG. For the intrinsic construction, we prove the equivalence of the ordered, local, and global directed Markov properties, together with entropy, recursive-factorization, and logarithmic characterizations. The extrinsic construction always gives a normalized state and recovers each kernel as a conditional on all preceding systems. The same kernel, however, need not be recovered from the marginal on the vertex and its parents; a three-qubit example exhibits this obstruction. We prove that independence of the chosen topological ordering is sufficient exactly for transitive DAGs: every order-invariant kernel family then yields an intrinsically directed Markov state. Finally, we associate a logarithmic candidate with every positive definite state and DAG, prove that it is subnormalized, and show that the trace-one candidate is a directed Markov state. Both the candidate and the excess global information are invariant under DAG Markov equivalence.
Intervention theories of causality define a relationship as causal if appropriately specified interventions to manipulate a putative cause tend to produce changes in the putative effect. Interventionist causal theories are commonly formalized by using directed graphs to represent causal relationships, local probability models to quantify the relationship between cause and effect, and a special kind of conditioning operator to represent the effects of interventions. Such a formal model represents a family of joint probability distributions, one for each allowable intervention policy. This paper interprets the von Neumann formalization of quantum theory as an interventionist theory of causality, describes its relationship to interventionist theories popular in the artificial intelligence literature, and presents a new family of graphical models that extends causal Bayesian networks to quantum systems.