A new approach to causality and economic growth.

This paper examines the issue of causality in cross-sectional empirical models of economic growth. Using an approach to determining causal structures based on tests for conditional independence in sets of variables, we uncover alternative causal structures that are consistent with the correlation pattern of the variables in the data. We use these methods to develop alternative causal empirical models of economic growth. One of our consistent findings is that we can rule out the possibility that equipment investment causes growth. Our search procedure leads naturally to a structural model with latent variables which we then estimate. The results of our estimation are broadly consistent with traditional models of economic growth augmented for human capital.Economic development ; Econometric models

Model and World: Generalizing the Ontic Conception of Scientific Explanation

Model and World defends a theory of scientific explanation that I call the “Generalized Ontic Conception” (GOC), according to which a model explains when and only when it provides (approximately) veridical information about the ontic structures on which the explanandum phenomenon depends. Causal and mechanistic explanations are species of GOC in which the ontic structures on which the explanandum phenomenon depends are causes and mechanisms, respectively, and the kinds of dependence involved are causal and constitutive/mechanistic, respectively. The kind of dependence relation about which information is provided determines the species of the explanation. This provides an intuitive typology of explanations and opens the possibility for non-causal, non-mechanistic explanations that provide information about noncausal, non-mechanistic kinds of dependence (Pincock 2015; Povich forthcoming a). What unites all these forms of explanation is that, by providing information about the ontic structures on which the explanandum phenomenon depends, they all can answer what-if-things-had-beendifferent questions (w-questions) about the explanandum phenomenon. This is what makes causal explanations, mechanistic explanations, and non-causal, non-mechanistic explanations all explanations. Furthermore, GOC is a generalized ontic conception of scientific explanation (Salmon 1984, 1989; Craver 2014). It is consistent with Craver\u27s claim that (2014), according to the ontic conception, commitments to ontic structures (like causes or mechanisms) are required to demarcate explanation from other scientific achievements. GOC demarcates explanatory from non-explanatory models in terms of ontic structures. For example, the distinction between explanatory and phenomenal models is cashed out in terms of the ontic structures about which information is conveyed: A phenomenal model provides information about the explanandum phenomenon, but not the ontic structures on which it depends. GOC is generalized because it says that commitments to more of the ontic than just the causal-mechanical – the traditional focus of the ontic conception – are required adequately to achieve this demarcation; attention to all ontic structures on which the explanandum depends is required. The relation between model and world required for explanation is elaborated in terms of information rather than mapping, reference, description, or similarity (Craver and Kaplan 2011; Kaplan 2011; Weisberg 2013). The latter concepts prove too strong, so will not count models as explanatory that in fact are. Take Kaplan and Craver\u27s (2011) model-to-mechanism-mapping (3M) principle. According to 3M, the variables in a mechanistic explanatory model must map to specific structural components and causal interactions of the explanandum phenomenon\u27s mechanism. However, you can mechanistically explain without referring to the explanandum\u27s mechanism or its components and their activities, for example, by describing what the mechanism is not like. This is a way of constraining or conveying information about a mechanism without actually mapping to, referring to, describing, representing, or being similar to it

Towards a social ontology of market systems

Academic analyses of market systems are deeply divided. While economists tend to neglect the personal and sociological factors that shape the behaviour of market actors, sociologists tend to discount the possibility of a systematic analysis of the consequences of market interactions. Economists thus end up with unrealistic models of markets, and sociologists end up unable to explain the economic impact of markets. This paper outlines a project that aims to produce an analysis of markets that is both sociologically realistic and capable of explaining economic effects. The project will construct a realistic ontological analysis of market systems, developed using a critical realist methodology. Market systems, it will argue, are social structures that depend ontologically upon both human individuals and a number of normative institutions. These institutions tend to produce coordinated interactions between market actors, and these interactions underpin mechanisms that endow market systems with emergent causal powers. Different types of interactions underpin different market mechanisms, including mechanisms like those theorised by mainstream economists, but also others that they tend to neglect, and an adequate understanding of real-world markets depends on analysing these multiple mechanisms and how they interact. This will be a theoretical project in economic sociology, drawing on existing empirical work without conducting new empirical research. It will be focussed primarily on contemporary product markets in advanced capitalist economies, while selected historical and alternative contemporary models will be considered more briefly to illustrate both the historical specificity of the dominant contemporary model and the possibility of alternative types of market system

The lesson of causal discovery algorithms for quantum correlations: Causal explanations of Bell-inequality violations require fine-tuning

An active area of research in the fields of machine learning and statistics is the development of causal discovery algorithms, the purpose of which is to infer the causal relations that hold among a set of variables from the correlations that these exhibit. We apply some of these algorithms to the correlations that arise for entangled quantum systems. We show that they cannot distinguish correlations that satisfy Bell inequalities from correlations that violate Bell inequalities, and consequently that they cannot do justice to the challenges of explaining certain quantum correlations causally. Nonetheless, by adapting the conceptual tools of causal inference, we can show that any attempt to provide a causal explanation of nonsignalling correlations that violate a Bell inequality must contradict a core principle of these algorithms, namely, that an observed statistical independence between variables should not be explained by fine-tuning of the causal parameters. In particular, we demonstrate the need for such fine-tuning for most of the causal mechanisms that have been proposed to underlie Bell correlations, including superluminal causal influences, superdeterminism (that is, a denial of freedom of choice of settings), and retrocausal influences which do not introduce causal cycles.Comment: 29 pages, 28 figs. New in v2: a section presenting in detail our characterization of Bell's theorem as a contradiction arising from (i) the framework of causal models, (ii) the principle of no fine-tuning, and (iii) certain operational features of quantum theory; a section explaining why a denial of hidden variables affords even fewer opportunities for causal explanations of quantum correlation

Critical Realism and Statistical Methods: A Response to Nash

This article offers a defence of critical realism in the face of objections Nash (2005) makes to it in a recent edition of this journal. It is argued that critical and scientific realisms are closely related and that both are opposed to statistical positivism. However, the suggestion is made that scientific realism retains (from statistical positivism) a number of elements that result in misleading accounts of social processes and events: indicators are used which do not reflect the close relationship between structure and agency; indicators refer to reified and not real properties of both structures and agents; and indicators do not refer to causal properties of objects and entities. In order to develop a narrative of causal processes, as Nash argues researchers should, then some adjustments need to be made to the principles that underpin scientific realism

A Deflationary Account of Mental Representation

Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of mental content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts

Causal loops: logically consistent correlations, time travel, and computation

Causal loops are loops in cause-effect chains: An effect can be the cause of that effect's cause. We show that causal loops can be unproblematic, and explore them from different points of view. This thesis is motivated by quantum theory, general relativity, and quantum gravity. By accepting all of quantum theory one can ask whether the possibility to take superpositions extends to causal structures. Then again, quantum theory comes with conceptual problems: Can we overcome these problems by dropping causality? General relativity is consistent with space-time geometries that allow for time-travel: What happens to systems traveling along closed time-like curves, are there reasons to rule out the existence of closed time-like curves in nature? Finally, a candidate for a theory of quantum gravity is quantum theory with a different, relaxed space-time geometry. Motivated by these questions, we explore the classical world of the non-causal. This world is non-empty; and what can happen in such a world is sometimes weird, but not too crazy. What is weird is that in these worlds, a party (or event) can be in the future and in the past of some other party (time travel). What is not too crazy is that this theoretical possibility does not lead to any contradiction. Moreover, one can identify logical consistency with the existence of a unique fixed point in a cause-effect chain. This can be understood as follows: No fixed point is the same as having a contradiction (too stiff), multiple fixed points, then again, is the same as having an unspecified system (too loose). This leads to a series of results in that field: Characterization of classical non-causal correlations, closed time- like curves that do not restrict the actions of experimenters, and a self-referential model of computation. We study the computational power of this model and use it to upper bound the computational power of closed time-like curves. Time travel has ever since been term weird, what we show here, however, is that time travel is not too crazy: It is not possible to solve hard problems by traveling through time. Finally, we apply our results on causal loops to other fields: an analysis with Kolmogorov complexity, local and classical simulation of PR-box correlations with closed time-like curves, and a short note on self-referentiality in language