Non-causal Explanations Research Articles

A Defence of Manipulationist Noncausal Explanation: The Case for Intervention Liberalism

Recent years have seen growing interest in modifying interventionist accounts of causal explanation in order to characterise noncausal explanation. However, one surprising element of such accounts is that they have typically jettisoned the core feature of interventionism: interventions. Indeed, the prevailing opinion within the philosophy of science literature suggests that interventions exclusively demarcate causal relationships. This position is so prevalent that, until now, no one has even thought to name it. We call it “intervention puritanism”. In this paper, we mount the first sustained defence of the idea that there are distinctively noncausal explanations which can be characterized in terms of possible interventions; and thus, argue that I-puritanism is false. We call the resultant position “intervention liberalism” (I-liberalism, for short). While many have followed Woodward (Making Things Happen: A Theory of Causal Explanation, Oxford University Press, Oxford, 2003) in committing to I-pluralism, we trace support for I-liberalism back to the work of Kim (in: Kim (ed) Supervenience and mind, Cambridge University Press, Cambridge, 1974/1993). Furthermore, we analyse two recent sources of scepticism regarding I-liberalism: debate surrounding mechanistic constitution; and attempts to provide a monistic account of explanation. We show that neither literature provides compelling reasons for adopting I-puritanism. Finally, we present a novel taxonomy of available positions upon the role of possible interventions in explanation: weak causal imperialism; strong causal imperialism; monist intervention puritanism; pluralist intervention puritanism; monist intervention liberalism; and finally, the specific position defended in this paper, pluralist intervention liberalism.

Mathematical Explanations in Evolutionary Biology or Naturalism? A Challenge for the Statisticalist

This article presents a challenge that those philosophers who deny the causal interpretation of explanations provided by population genetics might have to address. Indeed, some philosophers, known as statisticalists, claim that the concept of natural selection is statistical in character and cannot be construed in causal terms. On the contrary, other philosophers, known as causalists, argue against the statistical view and support the causal interpretation of natural selection. The problem I am concerned with here arises for the statisticalists because the debate on the nature of natural selection intersects the debate on whether mathematical explanations of empirical facts are genuine scientific explanations. I argue that if the explanations provided by population genetics are regarded by the statisticalists as non-causal explanations of that kind, then statisticalism risks being incompatible with a naturalist stance. The statisticalist faces a dilemma: either she maintains statisticalism but has to renounce naturalism; or she maintains naturalism but has to content herself with an account of the explanations provided by population genetics that she deems unsatisfactory. This challenge is relevant to the statisticalists because many of them see themselves as naturalists.

In defence of explanatory realism

Explanatory realism is the view that explanations work by providing information about relations of productive determination such as causation or grounding. The view has gained considerable popularity in the last decades, especially in the context of metaphysical debates about non-causal explanation. What makes the view particularly attractive is that it fits nicely with the idea that not all explanations are causal whilst avoiding an implausible pluralism about explanation. Another attractive feature of the view is that it allows explanation to be a partially epistemic, context-dependent phenomenon. In spite of its attractiveness, explanatory realism has recently been subject to criticism. In particular, Taylor (Philos Stud 175(1):197–219, 2018). has presented four types of explanation that the view allegedly cannot account for. This paper defends explanatory realism against Taylor’s challenges. We will show that Taylor’s counterexamples are either explanations that turn out to provide information about entities standing in productive determination relations or that they are not genuine explanations in the first place.

Strength in causality: discerning causal mechanisms in the sufficient cause model.

The assessment of causality is fundamental to epidemiology and biomedical sciences. One well-known approach to distinguishing causal from noncausal explanations is the nine Bradford Hill viewpoints. A recent article in this journal revisited the viewpoints to incorporate developments in causal thinking, suggesting that the sufficient cause model is useful in elucidating the theoretical underpinning of the first of the nine viewpoints-strength of association. In this article, we discuss how to discern the causal mechanisms of interest in the sufficient cause model, which pays closer attention to the relationship between the sufficient cause model and the Bradford Hill viewpoints. To this end, we explicate the link between the sufficient cause model and the potential-outcome model, both of which have become the cornerstone of causal thinking in epidemiology and biomedicine. A clearer understanding of the link between the two models provides significant implications for interpretation of the observed risks in the subpopulations defined by exposure and confounder. We also show that the concept of potential completion times of sufficient causes is useful to fully discerning completed sufficient causes, which leads us to pay closer attention to the fourth of the nine Bradford Hill viewpoints-temporality. Decades after its introduction, the sufficient cause model may be vaguely understood and thus implicitly used under unreasonably strict assumptions. To strengthen our assessment in the face of multifactorial causality, it is significant to carefully scrutinize the observed associations in a complementary manner, using the sufficient cause model as well as its relevant causal models.

894Molecular epidemiology: building causal evidence within highly dimensioned studies

Abstract Focus of Presentation This presentation provides a practical perspective on molecular epidemiologic analysis within a single high dimensional study to consider a causal question. Findings The work steps include special issues. Those include composite exposures and understanding of collinearity across predictors. Laboratory artefact needs to be minimised. Non-causal confounding is also important. This can occur when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. The influence of laboratory processing features must be accounted for. This can be achieved by pre-processing measures to remove artefact or controlling for process factors. The work plan should allow the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. Conclusions/Implications A systematic approach is required to work through a question a question set and obtain insights on not only the exposure-disease association but the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies. Key messages A systematic set of work steps is outlined to assess whether detected exposure-disease associations have a low and high likelihood of being attributed to non-causal and causal explanations respectively.

Causal and non-causal explanations in code biology

In the philosophy of science, we can consider debates about the nature of non-causal explanations in general (e.g. Reutlinger, Saatsi 2018; Lange 2017) and then especially those in the life sciences (e.g. Huneman, 2018; Kostić 2020). These debates are accompanied by the development of a new mechanism that is becoming the major response to the nature of scientific explanation in the life sciences (e.g. Craver, Darden 2013; Craver 2006); and also by the development of a design explanation (e.g. Eck, Mennes 2016) that represents a modern variant of a functional explanation.In this paper, we will methodically:1. evaluate the plurality of explanatory strategies in contemporary science (chapter 2).2. describe the mechanical philosophy and mechanistic explanation (Glennan 2016; Craver, Darden 2013, etc.) (chapter 3).3. explicate the role of mechanisms in code biology (Barbieri 2015, 2002, etc.) and its relation to the new mechanism (chapter 4).4. fulfill the main goal of the paper – to apply mechanistic explanations in code biology (Barbieri 2019, etc.) and to apply their suitability for this scientific domain (chapter 5).

Towards a Universal Account of Asymmetry in Non-causal Explanations

Towards a Universal Account of Asymmetry in Non-causal Explanations

On Explaining Quantum Correlations: Causal vs. Non-Causal

At the basis of the problem of explaining non-local quantum correlations lies the tension between two factors: on the one hand, the natural interpretation of correlations as the manifestation of a causal relation; on the other, the resistance on the part of the physics underlying said correlations to adjust to the most essential features of a pre-theoretic notion of causation. In this paper, I argue for the rejection of the first horn of the dilemma, i.e., the assumption that quantum correlations call for a causal explanation. The paper is divided into two parts. The first, destructive, part provides a critical overview of the enterprise of causally interpreting non-local quantum correlations, with the aim of warning against the temptation of an account of causation claiming to cover such correlations ‘for free’. The second, constructive, part introduces the so-called structural explanation (a variety of non-causal explanation that shows how the explanandum is the manifestation of a fundamental structure of the world) and argues that quantum correlations might be explained structurally in the context of an information-theoretic approach to QT.

Reflection on modern methods: building causal evidence within high-dimensional molecular epidemiological studies of moderate size.

This commentary provides a practical perspective on epidemiological analysis within a single high-dimensional study of moderate size to consider a causal question. In this setting, non-causal confounding is important. This occurs when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. In particular, the influence of technical processing factors must be accounted for by pre-processing measures to remove artefact or to control for these factors such as batch run. Work steps include the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. A systematic approach is required to work through a question set and obtain insights on not only the exposure-disease association but also the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies.

Beyond Causal Explanation: Einstein's Principle Not Reichenbach's.

Our account provides a local, realist and fully non-causal principle explanation for EPR correlations, contextuality, no-signalling, and the Tsirelson bound. Indeed, the account herein is fully consistent with the causal structure of Minkowski spacetime. We argue that retrocausal accounts of quantum mechanics are problematic precisely because they do not fully transcend the assumption that causal or constructive explanation must always be fundamental. Unlike retrocausal accounts, our principle explanation is a complete rejection of Reichenbach’s Principle. Furthermore, we will argue that the basis for our principle account of quantum mechanics is the physical principle sought by quantum information theorists for their reconstructions of quantum mechanics. Finally, we explain why our account is both fully realist and psi-epistemic.

On the Ostrogradski Instability; or, Why Physics Really Uses Second Derivatives

Candidates for fundamental physical laws rarely, if ever, employ higher than second time derivatives. Easwaran (2014) sketches an enticing story that purports to explain away this puzzling fact and thereby provides indirect evidence for a particular set of metaphysical theses used in the explanation. I object to both the scope and coherence of Easwaran's account, before going on to defend an alternative, more metaphysically deflationary explanation: in interacting Lagrangian field theories, it is either impossible or very hard to incorporate higher than second time derivatives without rendering the vacuum state unstable. The so-called Ostrogradski instability represents a powerful constraint on the construction of new field theories and supplies a novel, largely overlooked example of non-causal explanation in physics.

Flagpoles anyone? Causal and explanatory asymmetries

This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.

Metaphysical explanations and the counterfactual theory of explanation

According to an increasingly popular view among philosophers of science, both causal and non-causal explanations can be accounted for by a single theory: the counterfactual theory of explanation. A kind of non-causal explanation that has gained much attention recently but that this theory seems unable to account for are grounding explanations. Reutlinger (Eur J Philos Sci 7(2):239-256, 2017) has argued that, despite these appearances to the contrary, such explanations are covered by his version of the counterfactual theory. His idea is supported by recent work on grounding by Schaffer and Wilson who claim there to be a tight connection between grounding and counterfactual dependence. The present paper evaluates the prospects of the idea. We show that there is only a weak sense in which grounding explanations convey information about counterfactual dependencies, and that this fact cannot plausibly be taken to reveal a distinctive feature that grounding explanations share with other kinds of explanations.

Childhood infection and subsequent risk of psychotic disorders in adults: A systematic review and meta-analysis.

The effects of childhood infection exposure on the risk of subsequent psychosis are unclear and no overview is available. We conducted a meta-analysis to assess the link between childhood infection and the risk of psychosis in later life. We performed systematic searches of the PubMed and Embase databases to identify relevant articles published up to March 1, 2020. Random-effects models were used to pool the odds ratios [OR] of childhood infection and later psychosis. Thirteen observational studies (seven on hospital exposure to infection and six on central nervous system (CNS) infection) were included in the meta-analysis. Hospital contact with any infection during childhood was associated with an increased risk of psychosis (OR, 1.27; 95 % confidence interval (CI), 1.13-1.44; p < 0.001; I2 = 84 %) or schizophrenia (OR, 1.44; 95 % CI, 1.19-1.73; p < 0.001; I2 = 54.2 %) later in life. In further analysis, the association also existed for children exposed to CNS infection (OR, 1.68; 95 % CI, 1.08-2.62; p = 0.021; I2 = 68.7 %). However, the risk was modulated by the timing and frequency of infection. Our results suggest an increased risk of psychosis later in life with infection exposure in childhood. However, non-causal explanations for the association cannot be ruled out.

The Prospects for a Monist Theory of Non-causal Explanation in Science and Mathematics

We explore the prospects of a monist account of explanation for both non-causal explanations in science and pure mathematics. Our starting point is the counterfactual theory of explanation (CTE) for explanations in science, as advocated in the recent literature on explanation. We argue that, despite the obvious differences between mathematical and scientific explanation, the CTE can be extended to cover both non-causal explanations in science and mathematical explanations. In particular, a successful application of the CTE to mathematical explanations requires us to rely on counterpossibles. We conclude that the CTE is a promising candidate for a monist account of explanation in both science and mathematics.

An Experimental Study of Structural Diversity in Social Networks

Several recent studies of online social networking platforms have found that adoption rates and engagement levels are positively correlated with structural diversity, the degree of heterogeneity among an individual's contacts as measured by network ties. One common theory for this observation is that structural diversity increases utility, in part because there is value to interacting with people from different network components on the same platform. While compelling, evidence for this causal theory comes from observational studies, making it difficult to rule out non-causal explanations. We investigate the role of structural diversity on retention by conducting a large-scale randomized controlled study on the Twitter platform. We first show that structural diversity correlates with user retention on Twitter, corroborating results from past observational studies. We then exogenously vary structural diversity by altering the set of network recommendations new users see when joining the platform; we confirm that this design induces the desired changes to network topology. We find, however, that low, medium, and high structural diversity treatment groups in our experiment have comparable retention rates. Thus, at least in this case, the observed correlation between structural diversity and retention does not appear to result from a causal relationship, challenging theories based on past observational studies.

Paradox as a Guide to Ground

AbstractI will use paradox as a guide to metaphysical grounding, a kind of non-causal explanation that has recently shown itself to play a pivotal role in philosophical inquiry. Specifically, I will analyze the grounding structure of the Predestination paradox, the regresses of Carroll and Bradley, Russell's paradox and the Liar, Yablo's paradox, Zeno's paradoxes, and a novel omega plus one variant of Yablo's paradox, and thus find reason for the following: We should continue to characterize grounding as asymmetrical and irreflexive. We should change our understanding of the transitivity of grounding in a certain sense. We should require foundationality in a new, generalized sense, that has well-foundedness as its limit case. Meta-grounding is important. The polarity of grounding can be crucial. Thus we will learn a lot about structural properties of grounding from considering the various paradoxes. On the way, grounding will also turn out to be relevant to the diagnosis (if not the solution) of paradox. All the paradoxes under consideration will turn out to be breaches of some standard requirement on grounding, which makes uniform solutions of large groups of these paradoxes more desirable. In sum, bringing together paradox and grounding will be shown to be of considerable value to philosophy.1

Network explanations and explanatory directionality.

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding.

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non-parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non-parametric model, and we propose multiply robust and locally efficient estimators when non-parametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post-licensure surveillance of vaccine safety among children.

Can Health Information Technology Save Lives During a Pandemic?

During the COVID-19 pandemic, US deaths per case decreased from 7.46% in April 2020 to 1.82% in September 2020. In addition to increased testing, a leading explanation for this decline is that hospitals learned over time how to better treat patients diagnosed with COVID-19. Hospitals use health information technologies (IT) to develop dynamic capabilities that enable greater organizational resilience based on health information sharing across providers. This suggests that health IT helps hospitals to save lives during a pandemic, especially during periods when knowledge about the disease improves over time. Using county-level data on health IT intensity, COVID-19 cases and deaths, we show that counties with greater hospital IT intensity exhibit fewer COVID-19 deaths. This result is strengthened when instrumenting for health IT intensity with measures of local internet quality. Using a battery of controls, we are able to exclude several non-causal explanations of health IT’s relationship to deaths, including county prosperity, demographics, COVID-19 cases, pre-COVID-19 hospital mortality rates, mobility and timing of pandemic exposure. Using fixed effects estimation, we observe that a standard deviation increase in a county’s health IT intensity is associated with .043 (95% CI [.068, .018]) fewer COVID-19 deaths per 1000 residents. Consistent with the learning hypothesis, we show that high-IT intensity counties are only significantly better at treating COVID-19 cases several months into the pandemic. Counties with hospitals that participate in COVID-19 related clinical trials also experienced faster learning. We provide evidence that faster learning from clinical trials is, locally, a substitute for learning from health IT, while health IT plays a role in helping hospitals learn from clinical trials in other counties, providing evidence of a spillover effect.