Michael Baumgartner

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

An actual cause of some token effect is itself a token event that helped to bring about that effect. The notion of an actual cause is different from that of a potential cause – for example a pre-empted backup – which had the capacity to bring about the effect, but which wasn't in fact operative on the occasion in question. Sometimes actual causes are also distinguished from mere background conditions: as when we judge that the struck match was a cause of the fire, while the presence of oxygen was merely part of the relevant background against which the struck match operated. Actual causation is also to be distinguished from type causation: actual causation holds between token events in a particular, concrete scenario; type causation, by contrast, holds between event kinds in scenario kinds

The first part of this paper argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation--for which it is explicitly designed--it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance or it gives rise to the unwanted consequence that constitution is a form of causation. The second part shows how Woodward’s theory can be adapted in such a way that (MM) neither undermines the mechanistic paradigm nor reduces constitution to causation. However, it turns out that this modified theoretical embedding of (MM) makes it impossible to produce empirical evidence for constitutive relations. The paper ends by suggesting an additional criterion, the fat-handedness criterion, which, when combined with (MM), generates indirect empirical evidence for constitutive relevance.

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.

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.

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.