Petri Ylikoski

When talking about causes we often think of an imagined contrast to the real sequence of events: we use a counterfactual, asking what would have happened if the cause had not occurred. But one might be doubtful about the explanatory force of this analysis. The basic problem is that the truth or falsity of a counterfactual statement cannot be determined by empirical means. In some cases, notably in physics, we can apply a strict law when justifying claims about alternative scenarios. In most cases, however, we have at best regularitiesRegularities and in such cases it is difficult to have any confidence in the corresponding counterfactual. An account of causation in terms of the more restricted concept of potential outcomes is much more useful. It is closer to empirical practice and is more reliable. The main points are: It is common to explain the meaning of A caused B’ as ‘If A had not occurred, then B would not have occurred.’This is no step forward, since we do not in general know whether the counterfactual is true.Rubin’s more restricted notion of potential outcome is to be preferred, since it can be applied to empirical research.

The terms ‘cause’ and ‘effect’ are very common in both ordinary and scientific discourse. Since they have a number of synonyms (or near synonyms), there is no point in trying to define ‘cause’ or ‘effect’ using any of these synonyms; ‘cause’ and ‘effect’ belong to the most fundamental level of language learnt in childhood. The way to give their meaning is to display a number of contexts in which causal expressions are used without any justification. The chapter ends with a presentation and discussion of Hume’s Humecriteria for the use of the term ‘cause’. The main points are: Causal expressions belong to our most basic vocabulary, learnt at an early age.Many causal expressions from ordinary language are used in scientific texts without being explicitly defined.It is not possible to define ‘cause’ and ‘effect’ in any more primitive vocabulary.What we can do is to describe some necessary conditions for the correct use of ‘cause’ and ‘effect’.

This chapter gives an overview of causal idiom in ordinary language and introduces some fundamental semantic distinctions. The main points are: The words ‘cause’ and ‘effect’ have quite a number of near synonyms.‘Cause’ and ‘effect’ are relational terms.Causal relations relate three kinds of things: events, categories of events and variables used in statistical treatments of such categories.A mathematical relation between variables does not in itself tell us whether they are causally related or not.

The book has so far introduced fundamental ideas about causation, i.e., the relation between cause and effect, from philosophy, particularly those ideas that underlie studies of causation based on quantitative data and statistical methods of causal inference (Chaps. 1–7). Knowledge of these concepts, ideas and associated methods is essential as they are often used in sustainability science studies rooted in the natural sciences, economics and other quantitative social sciences. The book has also introduced the notions of causal explanation and causal mechanisms, which are used more broadly in both quantitative and qualitative studies to explain how a cause brings about an effect (Chap. 8). In this last chapter we want to reflect on causal reasoning from a broader angle, to illustrate the diversity of ways in which sustainability researchers reason about causation, and to highlight the many instances within a research process in which researchers engage in causal reasoning.

In this chapter we explore the connections between on the one hand causal relations and on the other hand strict and less strict laws, i.e., regularities, expressed as correlations and regressions.It is tempting to think that laws and regularities describe general causal relations. They do not. Neither laws nor regularities distinguish between cause and effect, they state relations between quantities only; the causal aspect is connected to the manipulation and this aspect is not represented in formulations of laws and regularities.Non-strict laws, often called ‘regularities’, differ from strict laws in that they are conditioned on ceteris paribus clauses, i.e., unspecified clauses of the form ‘all else the same’. This makes generalisations, i.e., inferences to unobserved situations, difficult.The main points of this chapter are: Laws, strict and non-strict, express relations between quantities, not cause-effect relations.Regularities are expressed by two measures, coefficient of correlation and regression.Inferring causal relations from laws and regularities requires additional information.Having such information one may represent causal relations using directed graphs and/or structural equations.

There is no such class of things as causes. The terms ‘cause’ and ‘effect’ are relational terms, in other words, our concern is the causal relation ‘x causes y’. This relation is applied to pairs of singular events and states of affairs, to pairs of types of events/and states of affairs and to pairs of variables. In science we are mostly interested in generalities, so the focus is usually how to infer causal relations between types of events/states of affairs and between variables.The main points in this chapter are: Both singular and general events/states of affairs can be related as cause and effect.Expressions for laws, equations, correlations and conditional probabilities do not by themselves state any causal relations. A causal interpretation of these requires information not contained in that law, equation, correlation or probability statement.Without background information it is impossible to infer a causal relation about a singular case, no matter whether the data are quantitative or qualitative.Most events have many causes, and such situations can be analysed in terms of INUS-conditions.

Empirical results often consist of data organised as values of variables.The first question is whether an observed correlation is evidence enough for a correlation in the entire population. If the answer is yes, the next question is whether this correlation reflects a causal connection or not. That need not be the case, there might be a common cause. The main points of this chapter are: A correlation in an observed sample may not reflect any correlation in the entire population.There are three possible explanations for a correlation in a population: variable X is one cause of Y, or vice versa, or they have a common cause.The most reliable way to decide which is the case is to perform experiments.If experiments are impossible one can sometimes infer a causal relation from a natural experiment.

There are several forms of explanation, one of which being causal explanations. Causal explanations are often descriptions of mechanisms, i.e., descriptions of how a state change in one object, labelled ‘the cause’, is transmitted through a number of intermediate objects to the final effect, i.e., a state change in another object. So the fundamental structure of mechanistic explanations is that of chained cause-effect relations.The main points of this chapter are: Causal explanation is one kind of explanation beside several other kinds.A causal explanation often consists of describing the mechanism by which the cause produces its effect.Reasons for human actions are often viewed as causes of those actions, but that is controversial.Three types of causal explanations in terms of mechanisms are confounder mechanisms, feedbacks, Feedback and bifurcations.

This open access book is about causal thinking and the use of causal language, with a focus on introducing philosophical ideas about causation to students and researchers of Social-Ecological Systems (SES). It takes a systematic approach to three central topics: the meanings of different causal expressions, sufficiency of evidence for inferences from observations to causal relations, and how to handle the complexity of causal relations in social-ecological systems. Consequently, the book is divided into three parts. In the first part the authors analyse and discuss the use of causal idiom in ordinary language, and in the second part they scrutinise the use of causal concepts and causal inference in science. Finally, the authors discuss causal reasoning about social-ecological systems in multi- and interdisciplinary contexts. This book provides an analysis of the concept of causation useful in the empirical sciences, where causal notions and idioms often are used without sufficient reflection. Empirical sciences often use causal idiom drawn from ordinary language, and similarly there is little formalisation of causal language and technical concepts in the humanities and social sciences. This book is a valuable resource for the application of current philosophical discussions about the concept of causation, in particular when applied to the analysis of social-ecological systems, but also when applied to research in the sciences and humanities.

In this chapter I will employ a well-known scientific research heuristic that studies how something works by focusing on circumstances in which it does not work. Rather than trying to describe what scientific understanding would ideally look like, I will try to learn something about it by observing mundane cases where understanding is partly illusory. My main thesis is that scientists are prone to the illusion of depth of understanding (IDU), and as a consequence they sometimes overestimate the detail, coherence, and depth of their understanding. I will analyze the notion of understanding and its relation to a sense of understanding. In order to make plausible the claim that these are often disconnected, I will describe an interesting series of psychological experiments by Frank Keil and his coauthors that suggests that ordinary people routinely overestimate the depth of their understanding. en I will argue that we should take seriously the possibility that scientific cognition is also aðected by IDU and spell out some possible causes of explanatory illusions in science. I will conclude this chapter by discussing how scientific explanatory practices could be improved and how the philosophy of science might be able to contribute to this process.

Sociology aims to explain the emergence, persistence, and change of larger-scale social events, entities, properties, and processes. A vital element of this explanatory task is to connect the larger-scale changes to smaller-scale social properties and processes. Philosophers of social science usually employ the idea of a level to analyze the relation between macro and micro, which leads to misunderstanding the sociological micro-macro problem. Philosophical arguments often assimilate the micro-macro challenge into the problem of methodological individualism and treat it as analogical to the relation between the physical and the mental. This presupposes that there is an individual level that is unique, comprehensive, and privileged, but these conditions are not satisfied. As an alternative, one should pay attention to the pragmatics of the micro-macro distinction, which is flexible, local, and relative. Once this is taken seriously, the motivation for assuming levels vanishes. Levels thinking creates problem substitutions rather than problem solutions. Furthermore, it supports a sterile style of theorizing. As an alternative, the paper suggests thinking about micro and macro in terms of scale and recognizing their heterogeneity. The concept of level is a placeholder, rather than as a theoretical concept. While figuring out relations between micro and macro is vital in the social sciences, it can be practiced without postulating levels. The paper concludes that a skeptical strategy towards levels is justified in the social sciences as the concept is heuristically misleading. To sustain necessary conceptual vigilance, social scientists should adopt the meta-heuristic of avoiding it.

When reasoning about causes of sustainability problems and possible solutions, sustainability scientists rely on disciplinary-based understanding of cause–effect relations. These disciplinary assumptions enable and constrain how causal knowledge is generated, yet they are rarely made explicit. In a multidisciplinary field like sustainability science, lack of understanding differences in causal reasoning impedes our ability to address complex sustainability problems. To support navigating the diversity of causal reasoning, we articulate when and how during a research process researchers engage in causal reasoning and discuss four common ideas about causation that direct it. This articulation provides guidance for researchers to make their own assumptions and choices transparent and to interpret other researchers’ approaches. Understanding how causal claims are made and justified enables sustainability researchers to evaluate the diversity of causal claims, to build collaborations across disciplines, and to assess whether proposed solutions are suitable for a given problem.

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies.

In this Chapter, we address the challenge of explaining institutional change, asking whether the much-criticized rational choice perspective can contribute to the understanding of institutional change in political science. We discuss the methodological reasons why rational choice institutionalism (RCI) often assumes that institutional change is exogenous and discontinuous. We then identify and explore the possible pathways along which RCI can be extended to be more useful in understanding institutional change in political science. Finally, we reflect on what RCI theorizing would look like if it started to take endogenous change seriously: by giving up some of its simplifying assumptions, RCI can be a useful tool for analyzing institutional change, but choosing this path has consequences for the generality of the models in RCI as well as for the style of its theorizing.

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work.

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that our account could be fruitfully used in appraising other models in philosophy and science.

Yhteiskuntatieteellinen tutkimus on pohjimmiltaan kysymyksiin vastaamista. Kysymysten avulla hahmotellaan yhteiskunnallisille ilmiöille syitä ja seurauksia. Mikä rooli syy-seuraussuhteiden ymmärtämisellä sitten on arvioitaessa ja täsmennettäessä yhteiskuntatieteellisiä selitysmalleja? Kausaalinen järkeily ja selittäminen läpäisee kaikkea yhteiskuntatieteellistä tutkimusta tutkimusmenetelmistä ja -asetelmista riippumatta. Kausaalisuuden käsitettä, kausaalisuhteita ja kausaalisia päätelmiä koskevien oletusten hahmottaminen on tärkeää, kun alan tutkimuskäytäntöjä kehitetään. Syistä selityksiin tarjoaa tieteenfilosofisia ja metodologisia välineitä, joita tarvitaan yhteiskuntatieteellisessä selittämisessä, kausaalianalyysissa ja pyrittäessä ratkaisemaan tiedon soveltamiseen liittyviä kysymyksiä. Kirjassa tarkastellaan kausaalisen järkeilyn merkitystä erilaisia tutkimusasetelmia ja -menetelmiä käyttävissä yhteiskuntatieteellisissä tutkimuksissa. Teorioita, käsitteitä ja metodologisia näkemyksiä havainnollistetaan lukuisilla konkreettisilla tutkimusesimerkeillä. Teos on suunnattu erityisesti yhteiskuntatieteiden ja niiden filosofian opiskelijoille sekä alan tutkijoille.

Generalization from a case study is a perennial issue in the methodology of the social sciences. The case study is one of the most important research designs in many social scientific fields, but no shared understanding exists of the epistemic import of case studies. This article suggests that the idea of mechanism-based theorizing provides a fruitful basis for understanding how case studies contribute to a general understanding of social phenomena. This approach is illustrated with a re- construction of Espeland and Sauder's case study of the effects of rankings on US legal education. On the basis of the reconstruction, it is argued that, at least with respect to sociology, the idea of mechanism-based theorizing captures many of the generalizable elements of case studies.

We compare Guala’s unified theory of institutions with that of Searle and Greif. We show that unification can be many things and it may be associated with diverse explanatory goals. We also highlight some of the important shortcomings of Guala’s account: it does not capture all social institutions, its ability to bridge social ontology and game theory is based on a problematic interpretation of the type-token distinction, and its ability to make social ontology useful for social sciences is hindered by Guala’s interpretation of social institution types as social kinds akin to natural kinds.