Non-causal Explanations Research Articles

Some Concerns Regarding Explanatory Pluralism: The Explanatory Role of Optimality Models

Optimality models are widely used in different parts of biology. Two important questions that have been asked about such models are: are they explanatory and, if so, what type of explanations do they offer? My concern in this paper is with the approach of Rice (2012, 2015) and Irvine (2015), who claim that these models provide non-causal explanations. I argue that there are serious problems with this approach and with the accounts of explanation it is intended to justify. The idea behind this undertaking is to draw attention to an important issue associated with the recent pluralist stance on explanation: the rampant proliferation of theories of explanation. This proliferation supports a pluralist perspective on explanation, and pluralism encourages such a proliferation. But, if we are not careful about how we arrive at and how we justify new accounts of explanation — i.e., if we do not try to avoid the sort of problems discussed in this paper — we may end up trivializing the concept of explanation.

Infection, antibiotic exposure, and risk of celiac disease: A systematic review and meta-analysis.

There is evidence of a relationship between infection (and the associated antibiotic exposure) and the risk of celiac disease (CD). This study performed a meta-analysis to investigate this relationship. To identify relevant studies, we conducted systematic searches of the PubMed, Embase, and Cochrane databases for articles published up to April 2019. Random effects models were used to determine overall pooled estimates and 95% confidence intervals (CIs). The meta-analysis included 19 observational studies (15 on infection and six on antibiotic exposure). Our results showed that any infection was associated with an increased risk of CD later in life (odds ratio, 1.37; 95% CI: 1.2-1.56; P < 0.001). The I2 was 94% (high heterogeneity among studies). Subgroup analyses suggested that the risk of CD is not affected by the type of infectious agent, timing of exposure, and site of infection. Exposure to antibiotics was also associated with new-onset CD (odds ratio, 1.2; 95% CI: 1.04-1.39; P < 0.001). Exposure to early infection or antibiotic appears to increase the odds of developing CD, suggesting that intestinal immune or microbiota dysbiosis may play a role in the pathogenesis of CD. These findings may influence clinical management and primary prevention of CD. However, noncausal explanations for these positive associations cannot be excluded.

Reassessing equilibrium explanations: When are they causal explanations?

Equilibrium explanations use an equilibrium to represent and explain a system’s dynamic behavior. They provide a system with the property of global stability: a system will converge towards and remain in equilibrium regardless of its initial conditions and dynamic process. Thus, equilibrium explanations are generally treated as non-causal explanations. There are two claims subsumed under that comprehensive thesis. The first claim is that equilibrium explanations do not identify any causes because a system with global stability resists manipulation. The second claim is that even if equilibrium explanations do identify causes by manipulation, those causes are embedded in a system’s deeper, underlying structural relationships, and those causes are irrelevant to explaining a system’s behavior. Only the system’s structural relationships are relevant. But equilibrium explanations are not monolithic. I compare dynamic systems with multiple, competing equilibria to systems with a globally stable equilibrium. Equilibrium explanations of the former use intervention on a system’s initial conditions and dynamic process to manipulate equilibria, which identifies causes. Furthermore, a system’s initial conditions and dynamic process are relevant to explaining why one equilibrium is selected instead of another. I then apply these lessons to systems with a globally stable equilibrium and discuss when their corresponding equilibrium explanations have a proper role in the parent sciences.

Asymmetry as a challenge to counterfactual accounts of non-causal explanation

This paper examines some recent attempts that use counterfactuals to understand the asymmetry of non-causal scientific explanations. These attempts recognize that even when there is explanatory asymmetry, there may be symmetry in counterfactual dependence. Therefore, something more than mere counterfactual dependence is needed to account for explanatory asymmetry. Whether that further ingredient, even if applicable to causal explanation, can fit non-causal explanation is the challenge that explanatory asymmetry poses for counterfactual accounts of non-causal explanation. This paper argues that several recent accounts (Woodward, in: Reutlinger and Saatsi (eds) Explanation beyond causation: philosophical perspectives on non-causal explanations, Oxford University Press, Oxford, pp 117–140, 2018; Jansson and Saatsi in Br J Philos Sci, forthcoming; Jansson in J Philos 112:7–599, 2015; Saatsi and Pexton in Philos Sci 80: 613–624, 2013; French and Saatsi, in: Reutlinger and Saatsi (eds) Explanation beyond causation: philosophical perspectives on non-causal explanations, Oxford University Press, Oxford, pp 185–205, 2018) fail to meet this challenge. The paper then sketches a more positive proposal for dealing with explanatory asymmetry in non-causal explanations.

Resisting Non-causal Explanations

Resisting Non-causal Explanations

Universality caused: the case of renormalization group explanation

Recently, many have argued that there are certain kinds of abstract mathematical explanations that are noncausal. In particular, the irrelevancy approach suggests that abstracting away irrelevant causal details can leave us with a noncausal explanation. In this paper, I argue that the common example of Renormalization Group (RG) explanations of universality used to motivate the irrelevancy approach deserves more critical attention. I argue that the reasons given by those who hold up RG as noncausal do not stand up to critical scrutiny. As a result, the irrelevancy approach and the line between casual and noncausal explanation deserves more scrutiny.

Unifying the Debates: Mathematical and Non-Causal Explanations

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the question what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e. explanations that don’t cite causes in the explanans) sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about counterfactuals, modality and explanatory asymmetries, i.e. do mathematical and non-causal explanations support counterfactuals, and how to understand explanatory asymmetries in non-causal explanations. Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology, where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in the philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is whether in network models in biology, ecology, neuroscience, and computer science the mathematical or more precisely topological properties can be explanatory of physical phenomena, or they are just different ways to represent causal structures. The aim of the special issue is to unify all these debates around several overlapping questions.

Shades of Grey: Granularity, Pragmatics, and Non-Causal Explanation

Implicit contextual factors mean that the boundary between causal and non-causal explanation is not as neat as one might hope: as the phenomenon to be explained is given descriptions with varying d...

Multilevel Ensemble Explanations: A Case from Theoretical Biology

I analyze a well-known argument by Stuart Kauffman about complex systems and evolution to show it contains a hierarchy of non-mechanistic, non-causal explanations—which I would call, following Kauf...

Are There Both Causal and Non-Causal Explanations of a Rocket’s Acceleration?

A typical textbook explanation of a rocket’s motion when its engine is fired appeals to momentum conservation: the rocket accelerates forward because its exhaust accelerates rearward and the system...

42 - LONGITUDINAL PATTERNS OF ALCOHOL USE AND MORTALITY - A 40 YEAR FOLLOW-UP OF THE FINNISH TWIN COHORT

Background Although the relationship of alcohol use with mortality has been extensively studied, these epidemiological studies are mainly based on single assessments of alcohol use and do not differentiate between causal and non-causal explanations. With four decades of follow-up in the older Finnish Twin Cohort, we examined the relationship of mortality with patterns of alcohol use in a genetically informative sample of MZ and DZ pairs. Methods Three questionnaire surveys of the Finnish Twin Cohort of like-sex twin pairs born 1930–1957 were conducted in 1975, 1981 & 1990. Alcohol consumption was assessed in an identical fashion in all surveys as the use of beer, wine and spirits. These were converted to the number of standard drinks consumed on average. Based on responses to all three surveys from 11,382 men and women, we first analysed the average consumption, ranging from abstinence over all 15 years (29% of all) to an average of 28 or more drinks per week (2%). Next, 3,656 persons whose consumption level (abstinent, one drink per week, 2–6, 7–13 or 14 or more drinks weekly) was the same across all time points were studied. Total mortality was obtained for response date in 1990 to end of follow-up 12/31/2016 with 1,977 deaths. Individual and within pair analyses were conducted using Cox proportional hazards regression models to derive hazard ratios (HR) for death. Results In analyses of all individuals, the lowest age-sex adjusted mortality was found for those drinking 1 drink per week on average, while consistent abstainers (HR 1.27,95%CI 1.09 to 1.49) and those consuming more had significantly elevated risks. The HR for 14 to 20 drinks weekly was 2.95, 21 to 27 drinks/wk had an HR of 4.42 while consuming 28 or more drinks/wk on average between 1975 and 1990 had an HR of 5.42 (95%CI 4.14to 7.10). For those with stable drinking patterns (n=3,656 twins, 648 deaths) the pattern was similar: those consistently drinking just one drink weekly had the lowest mortality, HR elevated to 1.49 for consistent abstainers; those drinking 2–6 drinks weekly had an HR of 1.43, 7 to 13 drinks an HR of 3.23, and those drinking at least 14 drinks at all three surveys an HR of 4.91. Within-family analyses found a pattern of mortality similar to the individual-based analyses, with an HR of 6.38 (3.14 to 13.0) for those twins consuming, on average, 28 or more drinks weekly in the prior 15 years compared to their co-twins drinking one drink weekly on average, with estimates for intermediate amounts lying between the two extremes. Discussion Our within-pair analyses were consistent with the individual analyses, with increased risks at the high end of use, but with a significant increase in mortality already at levels considered by many to represent safe drinking (i.e. 7 to 13 drinks weekly). Interestingly the results were similar and significant in both MZ and DZ pairs. The increased mortality associated with alcohol use is not due to genetic or other influences shared by co-twins but compatible with a causal effect.

VARIETIES OF DIFFERENCE-MAKERS: CONSIDERATIONS ON CHIRIMUUTA’S APPROACH TO NON-CAUSAL EXPLANATION IN NEUROSCIENCE

Abstract Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal difference-makers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. The problem is that efficient coding models also involve difference-makers that, prima facie, cannot be characterized as non-causal in this sense. Mathematical principles always involve variables which have counterfactual (instead of counterpossible) relations between them. However, we cannot simply assume that these difference-makers are causal. They can also be found in paradigmatic non-causal explanations and therefore they must be characterized as non-causal in some sense. I argue that, despite appearances, Chirimuuta's view can be applied to these cases. The mentioned counterfactual relations presuppose the counterpossible conditionals that describe the modification of a relevant mathematical principle. If these conditionals are the hallmark of non-causal relations, then Chirimuuta’s criterion has the desired implication that variables in mathematical principles are non-causal difference-makers.

To be or not to be a carcinogen; delving into the glyphosate classification controversy

The International Agency for Research on Cancer (IARC) placed the most widely used herbicide glyphosate (GLY) into the category 2A (probably carcinogenic to humans), a classification questioned by experts from academia and industry. This article critically appraised the epidemiological and experimental data that led the IARC working group (WG) to consider GLY a probable human carcinogen and the ensuing controversy. An association of GLY with non-Hodgkin lymphoma was suggested by some observational studies. A non-causal explanation for this weak association, however, cannot be excluded. Contrary to WG’s view, long-term rodent assays yielded no convincing evidence that GLY is carcinogenic. The mechanistic evidence remains elusive as well. Bacterial reverse mutation tests (including tester strains sensitive to oxidative mutagens) were clearly negative, and so were rodent genotoxicity assays by oral route. Tests with mammalian cells in vitro yielded conflicting results at high (cytotoxic) concentrations of GLY-based formulations. Conflicting results were also obtained when high doses of GLY-based herbicides were administered to rodents by the intraperitoneal route. Such high doses are unlikely to be attained in realistic scenarios of exposure. Finally, the IARC classification is based on a conjectural hazard, and rational public health interventions must be based on estimated risks.

Association between periodontitis and risk of Alzheimer's disease, mild cognitive impairment and subjective cognitive decline: A case-control study.

To test the hypothesis that periodontal disease contributes to increased risk of mild cognitive impairment (MCI), subjective cognitive decline (SCD) and Alzheimer's disease (AD). This case-control study was conducted over a 3-year period in the municipality of Huddinge, Sweden. In total, 154 cases were consecutively enrolled from the Karolinska Memory Clinic at the Karolinska University Hospital and allotted to three diagnostic groups: AD, MCI and SCD, collectively referred to as "cases." Seventy-six cognitively healthy age- and gender-matched controls were randomly sampled through the Swedish population register. All cases and controls underwent clinical and radiographic oral examinations. Statistical analysis was based on logistic regression models adjusted for potential confounders. Poor oral health and marginal alveolar bone loss were more prevalent among cases than among controls. The cases group was associated with generalized marginal alveolar bone loss (odds ratio [OR]=5.81; 95% confidence interval [CI]=1.14-29.68), increased number of deep periodontal pockets (OR=8.43; CI 4.00-17.76) and dental caries (OR=3.36; CI 1.20-9.43). The results suggest that marginal periodontitis is associated with early cognitive impairment and AD. However, the study design does not preclude noncausal explanations.

Counterfactuals and Explanatory Pluralism

Abstract Recent literature on non-causal explanation raises the question as to whether explanatory monism, the thesis that all explanations submit to the same analysis, is true. The leading monist proposal holds that all explanations support change-relating counterfactuals. We provide several objections to this monist position. 1Introduction2Change-Relating Monism's Three Problems3Dependency and Monism: Unhappy Together4Another Challenge: Counterfactual Incidentalism4.1High-grade necessity4.2Unity in diversity5Conclusion

Because without Cause: Non-Causal Explanations in Science and Mathematics

Because without Cause: Non-Causal Explanations in Science and Mathematics

The development of renormalization group methods for particle physics: Formal analogies between classical statistical mechanics and quantum field theory

Analogies between classical statistical mechanics and quantum field theory (QFT) played a pivotal role in the development of renormalization group (RG) methods for application in the two theories. This paper focuses on the analogies that informed the application of RG methods in QFT by Kenneth Wilson and collaborators in the early 1970’s (Wilson and Kogut in Phys Rep 12:75–200, 1974). The central task that is accomplished is the identification and analysis of the analogical mappings employed. The conclusion is that the analogies in this case study are formal analogies, and not physical analogies. That is, the analogical mappings relate elements of the models that play formally analogous roles and that have substantially different physical interpretations. Unlike other cases of the use of analogies in physics, the analogical mappings do not preserve causal structure. The conclusion that the analogies in this case are purely formal carries important implications for the interpretation of QFT, and poses challenges for philosophical accounts of analogical reasoning and arguments in defence of scientific realism. Analysis of the interpretation of the cutoffs is presented as an illustrative example of how physical disanalogies block the exportation of physical interpretations from from statistical mechanics to QFT. A final implication is that the application of RG methods in QFT supports non-causal explanations, but in a different manner than in statistical mechanics.

Associations of myeloid hematological diseases of the elderly with osteoporosis: A longitudinal analysis of routine health care data

BackgroundMyelodysplastic Syndromes (MDS) and Acute Myeloid Leukemia (AML) are hematological stem cell diseases mainly of the elderly. Studies indicate a close relationship between bone metabolism and hematopoietic stem cells within the osteo-hematopoietic niche. However, it remains unclear how the disturbed interaction within the osteo-hematopoietic niche affects bone homeostasis in MDS and AML patients. MethodsWe utilized data of a large German statutory health insurance of approximately 2 million persons living in the German federal state of Saxony. Applying case definitions based on diagnosis, procedures and prescriptions we identified prevalent and incident cases with MDS, AML and osteoporosis (OSP) in persons aged ≥60 years. We applied time-to-event analyses to determine the relationship of MDS and AML with OSP with a specific focus on temporality. ResultsAmong all individuals aged ≥60 years (n = 891,095), 2.62% (n = 23,326), 0.14% (n = 1219) and 0.10% (n = 893) were identified with incident OSP, MDS and AML, respectively. The risk of incident OSP was significantly increased in patients with prevalent MDS (sex and age-adjusted model: HR = 1.87, 95%CI: 1.51–2.23). Conversely, patients with prevalent OSP had an increased risk to be diagnosed with incident MDS in the adjusted model (HR = 1.42, 1.19–1.65). For AML no significant associations were observed (adjusted models: inc. OSP with pre. AML; HR = 1.06, 0.65–1.47; inc. AML with pre. OSP; HR = 0.82, 0.41–1.23). DiscussionOur results could indicate a clinically relevant relationship between MDS and OSP in elderly patients, most likely resulting from a disturbed microenvironment within the osteo-hematopoietic niche. An alternative, non-causal explanation that MDS is caused by the medication prescribed for OSP can be partially ruled out, as the association between the two diseases remains if incident OSP cases are considered in patients with pre-existing MDS. These results need to be confirmed within other prospective studies and may allow then for comprehensive strategies for the prevention, early detection and clinical care of patients with MDS and OSP.

Adolescent Victimization and Early-Adult Psychopathology: Approaching Causal Inference Using a Longitudinal Twin Study to Rule Out Noncausal Explanations.

Adolescence is the peak age for both victimization and mental disorder onset. Previous research has reported associations between victimization exposure and many psychiatric conditions. However, causality remains controversial. Within the Environmental Risk Longitudinal Twin Study, we tested whether seven types of adolescent victimization increased risk of multiple psychiatric conditions and approached causal inference by systematically ruling out noncausal explanations. Longitudinal within-individual analyses showed that victimization was followed by increased mental health problems over a childhood baseline of emotional/behavioral problems. Discordant-twin analyses showed that victimization increased risk of mental health problems independent of family background and genetic risk. Both childhood and adolescent victimization made unique contributions to risk. Victimization predicted heightened generalized liability (the “p factor”) to multiple psychiatric spectra, including internalizing, externalizing, and thought disorders. Results recommend violence reduction and identification and treatment of adolescent victims to reduce psychiatric burden.

Because Without Cause: Non-Causal Explanations in Science and Mathematics, by Marc Lange

Because Without Cause: Non-Causal Explanations in Science and Mathematics, by Marc Lange