Michael Baumgartner

In philosophy of science, constitutive explanations have attracted much attention since Craver’s influential book Explaining the Brain (2007). His Mutual Manipulability (MM) theory of constitution aimed to explicate constitution as a non-causal explanatory relation and to demarcate between constituents and non-constituents. But MM received decisive criticism. In response, Craver et al. (2021) have recently proposed a new theory, called Matched Interlevel Experiments (MIE), which is currently gaining traction in various fields. The authors claim that MIE retains “the spirit of MM without conceptual confusion.” Our paper argues that this claim is not borne out: neither does MIE meet MM’s objectives nor is it free of conceptual confusion. At the same time, we show that it is possible to meet MM’s objectives in a conceptually sound manner—by adopting the so-called No De-Coupling theory of constitution.

The first part of this paper reveals a conflict between the core principles of deterministic causation and the standard method of difference, which is widely seen (and used) as a correct method of causally analyzing deterministic structures. We show that applying the method of difference to deterministic structures can giverise to causal inferences that contradict the principles of deterministic causation. The second part then locates the source of this conflict in an inference rule implemented in the method of difference according to which factors that can make a difference to investigated effects relative to one particular test setup are to be identified as causes, provided the causal background of the corresponding setup is homogeneous. The paper ends by modifying the method of difference in a way that renders it compatible with the principles of deterministic causation.

While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned to the minimalized deterministic dependencies.

While ideal interventions are acknowledged by many as valuable tools for the analysis of causation, recent discussions have shown that, since there are no ideal interventions on upper-level phenomena that non-reductively supervene on their underlying mechanisms, interventions cannot—contrary to a popular opinion—ground an informative analysis of constitution. This has led some to abandon the project of analyzing constitution in interventionist terms. By contrast, this paper defines the notion of a horizontally surgical intervention, and argues that, when combined with some innocuous metaphysical principles about the relation between upper and lower levels of mechanisms, that notion delivers a sufficient condition for constitution. This, in turn, strengthens the case for an interventionist analysis of constitution.

Alexander Gebharter has recently proposed to use Bayesian network causal discovery methods to identify the constitutive dependencies that underwrite mechanistic explanations. The proposal depends on using the assumptions of the causal Bayesian network framework to implicitly define mechanistic constitution as a kind of deterministic direct causal dependence. The aim of this paper is twofold. In the first half, we argue that Gebharter’s proposal incurs severe conceptual problems. In the second half, we present an alternative way to bring Bayesian network tools to bear on the issue of understanding mechanistic constitution. More precisely, our proposal interprets constitution as the relation explaining why a target phenomenon has its characteristic causal role in terms of the causal roles of some of its spatiotemporal parts---where the notion of causal role is probabilistically understood.

In "The Comparative Method" Ragin (1987) has outlined a procedure of Boolean causal reasoning operating on pure coincidence data that has meanwhile become widely known as QCA (Qualitative Comparative Analysis) among social scientists. QCA -- also in its recent form as presented in Ragin (2000) -- is designed to analyze causal structures featuring one effect and a possibly complex configuration of mutually independent direct causes of that effect. The paper at hand presents a procedure of causal reasoning that operates on the same type of empirical data as QCA and that implements Boolean techniques related to the ones resorted to by QCA, yet, in contrast to QCA, the procedure introduced here successfully identifies causal structures involving both mutually dependent causes, i.e. causal chains, and multiple effects, i.e. epiphenomena. In this sense, the paper at hand generalizes QCA.

The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention variables only if they also answer the questions that can be answered by interventionist techniques—which are thus rendered dispensable. The paper concludes by suggesting a way of identifying intervention variables that allows for exploiting the whole inferential potential of interventionist techniques.

According to a prevalent view among philosophers formal logic is the philosopher’s main tool to assess the validity of arguments, i.e. the philosopher’s ars iudicandi. By drawing on a famous dispute between Russell and Strawson over the validity of a certain kind of argument – of arguments whose premises feature definite descriptions – this paper casts doubt on the accuracy of the ars iudicandi conception. Rather than settling the question whether the contentious arguments are valid or not, Russell and Strawson, upon discussing the proper logical analysis of definite descriptions, merely contrast converse informal validity assessments rendered explicit by nonequivalent logical for-malizations.

For many years, sociologists, political scientists, and management scholars have readily relied on Qualitative Comparative Analysis (QCA) for the purpose of configurational causal modeling. However, this article reveals that a severe problem in the application of QCA has gone unnoticed so far: model ambiguities. These arise when multiple causal models fare equally well in accounting for configurational data. Mainly due to the uncritical import of an algorithm that is unsuitable for causal modeling, researchers have typically been unaware of the whole model space. As a result, there exists an indeterminable risk for practically all QCA studies published in the last quarter century to have presented findings that their data did not warrant. Using hypothetical data, we first identify the algorithmic source of ambiguities and discuss to what extent they affect different methodological aspects of QCA. By reanalyzing a published QCA study from rural sociology, we then show that model ambiguities are not a mere theoretical possibility but a reality in applied research, which can assume such extreme proportions that no causal conclusions whatsoever are possible. Finally, the prevalence of model ambiguities is examined by performing a comprehensive analysis of 192 truth tables across 28 QCA studies published in applied sociology. In conclusion, we urge that future QCA practice ensures full transparency with respect to model ambiguities, both by informing readers of QCA-based research about their extent and by employing algorithms capable of revealing them.

To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations with artificial data are unsuited for assessing QCA. We take issue with either position in this impasse. On the one hand, we argue that data-driven evaluations of the correctness of a procedure of causal inference require artificial data. On the other hand, we prove all previous attempts in this direction to have been defective. For the first time in the literature on configurational comparative methods, we lay out a set of formal criteria for an adequate evaluation of QCA before implementing a battery of inverse-search trials to test how this method performs in different recovery contexts according to these criteria. While our results indicate that QCA is correct when generating the parsimonious solution type, they also demonstrate that the method is incorrect when generating the conservative and intermediate solution type. In consequence, researchers using QCA for causal inference, particularly in human-sensitive areas such as public health and medicine, should immediately discontinue employing the method’s conservative and intermediate search strategies.

In recent years, the debate on the problem of causal exclusion has seen an ‘interventionist turn’. Numerous non-reductive physicalists (e.g. Shapiro and Sober 2007) have argued that Woodward's (2003) interventionist theory of causation provides a means to empirically establish the existence of non-reducible mental-to-physical causation. By contrast, Baumgartner (2010) has presented an interventionist exclusion argument showing that interventionism is in fact incompatible with non-reductive physicalism. In response, a number of revised versions of interventionism have been suggested that are compatible with non-reductive physicalism. The first part of this paper reconstructs the definitional details of these modified interventionist theories. The second part investigates whether the modification proposed in Woodward (2011) is not only compatible with, but moreover supports non-reductive physicalism. In particular, it is examined whether that newest variant of interventionism allows for empirically resolving the problem of causal exclusion as envisaged by Shapiro, Sober and others.

The majority of the currently flourishing theories of actual causation are located in a broadly counterfactual framework that draws on structural equations. In order to account for cases of symmetric overdeterminiation and preemption, these theories resort to rather intricate analytical tools, most of all, to what Hitchcock has labeled explicitly nonforetracking counterfactuals. This paper introduces a regularity theoretic approach to actual causation that only employs material conditionals, standard Boolean minimization procedures, and a stability condition that regulates the behavior of causal models under model expansions. Notwithstanding its lightweight analytical toolbox, this regularity theory performs at least as well as the structural equations accounts with their heavy appliances.

The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that is related to QCA, yet, contrary to the latter, does not eliminate redundancies by means of Quine-McCluskey optimization. The second part of the paper applies CNA to chain-generated data. It will turn out that CNA successfully detects causal chains in small-N data.

A natural language argument may be valid in at least two nonequivalent senses: it may be interpretationally or representationally valid (Etchemendy in The concept of logical consequence. Harvard University Press, Cambridge, 1990). Interpretational and representational validity can both be formally exhibited by classical first-order logic. However, as these two notions of informal validity differ extensionally and first-order logic fixes one determinate extension for the notion of formal validity (or consequence), some arguments must be formalized by unrelated nonequivalent formalizations in order to formally account for their interpretational or representational validity, respectively. As a consequence, arguments must be formalized subject to different criteria of adequate formalization depending on which variant of informal validity is to be revealed. This paper develops different criteria that formalizations of an argument have to satisfy in order to exhibit the latter’s interpretational or representational validity.

Gebharter has proposed using one of the best known Bayesian network causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic constitution behaves like deterministic direct causation, such that PC is directly applicable to mixed variable sets featuring both causal and constitutive dependencies. Gebharter claims that such mixed sets, under certain restrictions, comply with PC’s background assumptions. The aim of this article is to show that Gebharter’s proposal incurs severe problems, ultimately rooted in the widespread non-compliance of mechanistic systems with PC’s assumptions. This casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC’s assumptions.

A regularity theory of causation analyses type-level causation in terms of Boolean difference-making. The essential ingredient that helps this theoretical framework overcome the problems of Hume’s and Mill’s classical accounts is a principle of non-redundancy: only Boolean dependency structures from which no elements can be eliminated track causation. The first part of this article argues that the recent regularity-theoretic literature has not consistently implemented this principle, for it disregarded an important type of redundancies: structural redundancies. Moreover, it is shown that a regularity theory needs to be underwritten by a hitherto neglected metaphysical background assumption stipulating that the world’s causal makeup is not ambiguous. Against that background, the second part then develops a new regularity theory that does justice to all types of redundancies and, thereby, provides the first all-inclusive notion of Boolean difference-making.

The first part of this paper reveals a conflict between the core principles of deterministic causation and the standard method of difference, which is widely seen as a correct method of causally analyzing deterministic structures. We show that applying the method of difference to deterministic structures can give rise to causal inferences that contradict the principles of deterministic causation. The second part then locates the source of this conflict in an inference rule implemented in the method of difference according to which factors that can make a difference to investigated effects relative to one particular test setup are to be identified as causes, provided the causal background of the corresponding setup is homogeneous. The paper ends by modifying the method of difference in a way that renders it compatible with the principles of deterministic causation.

In philosophical contexts, logical formalisms are often resorted to as a means to render the validity and invalidity of informal arguments formally transparent. Since Oliver and Massey , however, it has been recognized in the literature that identifying valid arguments is easier than identifying invalid ones. Still, any viable theory of adequate logical formalization should at least reliably identify valid arguments. This paper argues that accounts of logical formalization as developed by Blau and Brun do not meet that benchmark. The paper ends by suggesting different strategies to remedy the problem

Causal slingshots are formal arguments advanced by proponents of an event ontology of token-level causation which, in the end, are intended to show two things: (i) The logical form of statements expressing causal dependencies on token level features a binary predicate ‘‘... causes...’’ and (ii) that predicate takes events as arguments. Even though formalisms are only revealing with respect to the logical form of natural language statements, if the latter are shown to be adequately captured within a corresponding formalism, proponents of slingshots usually take the adequacy of their formalizations for granted without justifying it. The first part of this paper argues that the most discussed version of a causal slingshot, viz. the one e.g. presented by Davidson (Essays on actions and events. Oxford, Clarendon Press, 1980), can indeed be refuted for relying on an inadequate formal apparatus. In contrast, the formal means of Gödel’s (The philosophy of Betrand Russell. New York, Tudor, 1944) often neglected slingshot are shown to stand on solid ground in the second part of the paper. Nonetheless, I contend that Gödel’s slingshot does only half the work friends of event causation would like it to do. It provides good reasons for (i) but not for (ii).

While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic struc- tures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned to the minimalized deterministic dependencies.

This paper argues that, notwithstanding the remarkable popularity of Woodward's (2003) interventionist analysis of causation, the exact definitional details of that theory are surprisingly little understood. There exists a discrepancy in the literature between the clarity about the logical details of interventionism, on the one hand, and the enormous work interventionism is expected to do, on the other. The first part of the paper distinguishes three significantly different readings of the logical form of Woodward's (2003) interventionist theory and identifies the reading that best captures the basic intuitions behind interventionism. In the second part, I show that this preferable reading is far from doing all the work that friends of interventionism would like it to do.

It has become a popular view among non-reductive physicalists that it is possible to devise empirical tests generating evidence for the causal efficacy of the mental, whereby the exclusion worries that have haunted the position of non-reductive physicalism for decades can be dissolved once and for all. This paper aims to show that these evidentialist hopes are vain. I argue that, if the mental is taken to supervene non-reductively on the physical, there cannot exist empirical evidence for its causal efficacy. While causal structures without non-reductive supervenience relations can be conclusively identified in ideal discovery circumstances, it is impossible, in principle, to generate evidence that would favour models with mental causation over models without. Ascribing causal efficacy to the mental, for the non-reductive physicalist, is a modelling choice that must be made on the basis of metaphysical background theories or pragmatic maxims guiding the selection among empirically indistinguishable models.

This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on non-empirical grounds.

According to the standard opinions in the literature, blocking the unacceptable consequences of the notorious slingshot argument requires imposing constraints on the metaphysics of facts or on theories of definite descriptions (or class abstracts). This paper argues that both of these well-known strategies to rebut the slingshot overshoot the mark. The slingshot, first and foremost, raises the question as to the adequate logical formalization of statements about facts, i.e. of factual contexts. It will be shown that a rigorous application of Quine’s maxim of shallow analysis to formalizations of factual contexts paves the way for an account of formalizing such contexts which blocks the slingshot without ramifications for theories of facts or definite descriptions.