Consistency of causal claims in observational studies: a review of papers published in a general medical journal

ObjectiveTo evaluate the consistency of causal statements in observational studies published in The BMJ.DesignReview of observational studies published in a general medical journal.Data sourceCohort and other longitudinal studies describing an exposure-outcome relationship published in The BMJ in 2018. We also had access to the submitted papers and reviewer reports.Main outcome measuresProportion of published research papers with ‘inconsistent’ use of causal language. Papers where language was consistently causal or non-causal were classified as ‘consistently causal’ or ‘consistently not causal’, respectively. For the ‘inconsistent’ papers, we then compared the published and submitted version.ResultsOf 151 published research papers, 60 described eligible studies. Of these 60, we classified the causal language used as ‘consistently causal’ (48%), ‘inconsistent’ (20%) and ‘consistently not causal’(32%). Eleven out of 12 (92%) of the ‘inconsistent’ papers were already inconsistent on submission. The inconsistencies found in both submitted and published versions were mainly due to mismatches between objectives and conclusions. One section might be carefully phrased in terms of association while the other presented causal language. When identifying only an association, some authors jumped to recommending acting on the findings as if motivated by the evidence presented.ConclusionFurther guidance is necessary for authors on what constitutes a causal statement and how to justify or discuss assumptions involved. Based on screening these papers, we provide a list of expressions beyond the obvious ‘cause’ word which may inspire a useful more comprehensive compendium on causal language.

Use of causal claims in observational studies: a research on research study

Objective: To evaluate the consistency of causal statements in the abstracts of observational studies published in The BMJ. Design: Research on research study. Data source: All cohort or longitudinal studies describing an exposure-outcome relationship published in The BMJ during 2018. We also had access to the submitted papers and reviewer reports. Main outcome measures: Proportion of published research papers with 'inconsistent' use of causal language in the abstract. Papers where language was consistently causal or non-causal were classified as 'consistently causal' or 'consistently not causal', respectively; those where causality may be inferred were classified as 'suggests causal'. For the 'inconsistent' papers, we then compared the published and submitted version. Results: Of 151 published research papers, 60 described eligible studies. Of these 60, we classified the causal language used as 'consistently causal' (13%), 'suggests causal' (35%), 'inconsistent' (20%) and 'consistently not causal'(32%). The majority of the 'Inconsistent' papers (92%) were already inconsistent on submission. The inconsistencies found in both submitted and published versions was mainly due to mismatches between objectives and conclusions. One section might be carefully phrased in terms of association while the other presented causal language. When identifying only an association, some authors jumped to recommending acting on the findings as if motivated by the evidence presented. Conclusion: Further guidance is necessary for authors on what constitutes a causal statement and how to justify or discuss assumptions involved. Based on screening these abstracts, we provide a list of expressions beyond the obvious 'cause' word which may inspire a useful more comprehensive compendium on causal language.

The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST)

Motivation A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. Results Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. Availability and implementation The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST Supplementary information Supplementary data are available at Bioinformatics online.

The Minimum Information about a Molecular Interaction Causal Statement (MI2CAST)

A large variety of molecular interactions occurs between biomolecular components in cells. When one or a cascade of molecular interactions results in a regulatory effect, by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g., gene regulation) need to be described with a careful appreciation of molecular interactions that occur between entities. A proper description of this information enables archiving, sharing, and reuse by humans and for computational science. Various representations of causal relationships between biological components are currently used in a variety of resources. Here, we propose a checklist that accommodates current representations, and call it the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while assuring uniformity and interoperability of the data across resources.

The role of the argument by example in legislative debates of the European Parliament

This paper focuses on the role of the argument by example in the argumentation put forward by Members of the European Parliament. The argumentative patterns that come into being in legislative debates in the European Parliament depend for the most part on the problem-solving argumentation that is put forward in the opening speech by the rapporteur of the parliamentary committee report. Complex problem-solving argumentation consists of a premise stating that there is a problem (the problem statement) and a premise stating that the proposed legislation will solve the problem (the causal statement). In their contributions, MEPs who are in favor of the proposal will either defend the problem statement or the causal statement. This paper examines how an argument by example is used in order to defend the problem statement. The argument by example can be used to defend the existential presupposition as well as the normative presupposition in the problem-statement.

Some Applications of the Causal Statement Logic Programming in Machine Language

In modern science, machine language was developed on the basis of multidisciplinary, so it forms the nature of cross subject or multidisciplinary subject, especially it crosses with modern logic. Burks developed the logic of casual statement, and tried to apply to the constructing of machine language. While applying the logic of casual statement to machine language, philosophy of logical machine was put forward. Philosophy of logical machine takes an important role in guiding the development of the discipline of contemporary machine language and its practical applications.

ON FORMALIZING CAUSATION BASED ON CONSTANT CONJUNCTION THEORY

Constant conjunction theory of causation had been the dominant theory in philosophy for a long time and regained attention recently. This paper gives a logical framework of causation based on the theory. The basic idea is that causal statements are empirical, and are derived from our past experience by observing constant conjunction between objects. The logic is defined on linear time structures. A causal statement is evaluated at time points, such that its value depends on what has been in the past. We first give a semantics that contains basic conditions that, we think, must hold for a concept of causation, on which we define the minimal causal logic. Then we discuss its possible extensions for various concepts of causation. Complete deductive systems are given.

Reviewing recommendations of root cause analyses

Objective: To determine the opinion of medical and nursing clinicians of recommendations arising from root cause analyses (RCAs) conducted between 1 April 2003 and 30 September 2004 in one Sydney Area Health Service. Methods: Twelve doctors (response rate 86%) and 17 nurses (response rate 100%) reviewed 328 recommendations arising from 59 RCAs and completed a self-administered survey. Results: Nurses were significantly more likely than doctors to rate recommendations made by the original RCA team as ?relevant to the causal statement?, ?understandable?, ?measurable? and ?achievable?. Doctors and nurses involved in the original RCA were significantly more likely to state that recommendations would ?eliminate? or ?control? the risk of a similar event occurring in the future. Conclusions: This is one of the first studies to analyse RCA data at the area health service level. That nurses reviewed recommendations more favourably may have implications for successful adoption of recommendations at the clinical level. We recommend further detailed analyses of recommendations arising from RCAs in order to determine their usefulness to inform strategies for improved patient safety.

Reflections on the Interview Process in Evocative Settings

Revealed causal mapping (RCM) represents one of the best ways to study a phenomenon in a discovery or evocative setting. The RCM method provides rich data that facilitates a deeper understanding of the cognitive facets of a phenomenon not available with other methods. In this chapter I will share insights gained from conducting several interactively elicited causal mapping studies in the discovery and evocative research contexts. I address issues a researcher will encounter during in the interview process, the causal statement identification procedure, and the development of the coding scheme. I conclude with some thoughts on lessons learned in the field.