scholarly journals Non-linear Causal Inference Using Gaussianity Measures

2019 ◽  
pp. 257-299
Author(s):  
Daniel Hernández-Lobato ◽  
Pablo Morales-Mombiela ◽  
David Lopez-Paz ◽  
Alberto Suárez
Keyword(s):  
Causal Inference ◽  
Non Linear

scholarly journals Are there non-linear relationships between alcohol consumption and long-term health? Protocol for a systematic review of observational studies employing approaches to improve causal inference

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043985
Author(s):  
Rachel Visontay ◽  
Matthew Sunderland ◽  
Tim Slade ◽  
Jack Wilson ◽  
Louise Mewton
Keyword(s):  
Systematic Review ◽  
Alcohol Consumption ◽  
Health Outcomes ◽  
Causal Inference ◽  
Study Design ◽  
Observational Studies ◽  
Alcohol Exposure ◽  
Non Linear ◽  
The Relationship

IntroductionThere is a substantial literature finding that moderate alcohol consumption is protective against certain health conditions. However, more recent research has highlighted the possibility that these findings are methodological artefacts, caused by confounding and other biases. While modern analytical and study design approaches can mitigate confounding and thus enhance causal inference in observational studies, they are not routinely applied in research assessing the relationship between alcohol use and long-term health outcomes. The purpose of this systematic review is to identify observational studies that employ these analytical/design-based approaches in assessing whether relationships between alcohol consumption and health outcomes are non-linear. This review seeks to evaluate, on a per-outcome basis, what these studies find the strength and form of the relationship between alcohol consumption and health to be.Methods and analysisElectronic databases (MEDLINE, PsycINFO, Embase and SCOPUS) were searched in May 2020. Study selection will comply with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles will be screened against eligibility criteria intended to capture studies using observational data to assess the relationship between varying levels of alcohol exposure and any long-term health outcome (actual or surrogate), and that have employed at least one of the prespecified approaches to enhancing causal inference. Risk of bias of included articles will be assessed using study design-specific tools. A narrative synthesis of the results is planned.Ethics and disseminationFormal ethics approval is not required given there will be no primary data collection. The results of the study will be disseminated through published manuscripts, conferences and seminar presentations.PROSPERO registration numberCRD42020185861.

scholarly journals Are there non-linear relationships between alcohol consumption and long-term health?: Protocol for a systematic review of observational studies employing approaches to improve causal inference

2020 ◽  
Author(s):  
Rachel Visontay ◽  
Matthew Sunderland ◽  
Tim Slade ◽  
Jack Wilson ◽  
Louise Mewton
Keyword(s):  
Systematic Review ◽  
Alcohol Consumption ◽  
Health Outcomes ◽  
Causal Inference ◽  
Study Design ◽  
Observational Studies ◽  
Alcohol Exposure ◽  
Non Linear ◽  
The Relationship

Introduction: There is a substantial literature finding that moderate alcohol consumption is protective against certain health conditions. However, more recent research has highlighted the possibility that these findings are methodological artefacts, caused by confounding and other biases. While modern analytical and study design approaches can enhance causal inference in observational studies, they are not routinely applied in research assessing the relationship between alcohol use and long-term health outcomes. The purpose of this systematic review is to identify observational studies that employ these analytical/design-based approaches in assessing whether relationships between alcohol consumption and health outcomes are non-linear. This review seeks to evaluate, on a per-outcome basis, what these studies find the strength and form of the relationship between alcohol consumption and health to be.Methods and analysis: Electronic databases (MEDLINE, PsycINFO, Embase and SCOPUS) were searched in May 2020. Study selection will comply with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles will be screened against eligibility criteria intended to capture studies using observational data to assess the relationship between varying levels of alcohol exposure and any long-term health outcome (actual or surrogate), and that have employed at least one of the pre-specified approaches to enhancing causal inference. Risk of bias of included articles will be assessed using study design-specific tools. A narrative synthesis of the results is planned.

scholarly journals Least-squares independence regression for non-linear causal inference under non-Gaussian noise

2013 ◽  
Vol 96 (3) ◽  
pp. 249-267 ◽  
Author(s):  
Makoto Yamada ◽  
Masashi Sugiyama ◽  
Jun Sese
Keyword(s):  
Causal Inference ◽  
Least Squares ◽  
Gaussian Noise ◽  
Non Linear ◽  
Non Gaussian

scholarly journals COVID-19-induced hyperinflammation, immunosuppression, recovery and survival: how causal inference may help draw robust conclusions

RMD Open ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. e001638
Author(s):  
Robert B M Landewé ◽  
Sofia Ramiro ◽  
Rémy L M Mostard
Keyword(s):  
Supportive Care ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Immunosuppressive Treatment ◽  
Experimental Treatment ◽  
Number Needed To Treat ◽  
Mediated Effects ◽  
Period 2 ◽  
Non Linear ◽  
Quasi Experimental

BackgroundThe CHIC study (COVID-19 High-intensity Immunosuppression in Cytokine storm syndrome) is a quasi-experimental treatment study exploring immunosuppressive treatment versus supportive treatment only in patients with COVID-19 with life-threatening hyperinflammation. Causal inference provides a means of investigating causality in non-randomised experiments. Here we report 14-day improvement as well as 30-day and 90-day mortality.Patients and methodsThe first 86 patients (period 1) received optimal supportive care only; the second 86 patients (period 2) received methylprednisolone and (if necessary) tocilizumab, in addition to optimal supportive care. The main outcomes were 14-day clinical improvement and 30-day and 90-day survival. An 80% decline in C reactive protein (CRP) was recorded on or before day 13 (CRP >100 mg/L was an inclusion criterion). Non-linear mediation analysis was performed to decompose CRP-mediated effects of immunosuppression (defined as natural indirect effects) and non-CRP-mediated effects attributable to natural prognostic differences between periods (defined as natural direct effects).ResultsThe natural direct (non-CRP-mediated) effects for period 2 versus period 1 showed an OR of 1.38 (38% better) for 14-day improvement and an OR of 1.16 (16% better) for 30-day and 90-day survival. The natural indirect (CRP-mediated) effects for period 2 showed an OR of 2.27 (127% better) for 14-day improvement, an OR of 1.60 (60% better) for 30-day survival and an OR of 1.49 (49% better) for 90-day survival. The number needed to treat was 5 for 14-day improvement, 9 for survival on day 30, and 10 for survival on day 90.ConclusionCausal inference with non-linear mediation analysis further substantiates the claim that a brief but intensive treatment with immunosuppressants in patients with COVID-19 and systemic hyperinflammation adds to rapid recovery and saves lives. Causal inference is an alternative to conventional trial analysis, when randomised controlled trials are considered unethical, unfeasible or impracticable.

scholarly journals 1168Are there non-linear alcohol–health relationships?: Review of observational studies employing enhanced causal methods

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Rachel Visontay ◽  
Matthew Sunderland ◽  
Tim Slade ◽  
Jack Wilson ◽  
Louise Mewton
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Alcohol Consumption ◽  
Health Outcomes ◽  
Causal Inference ◽  
Observational Studies ◽  
Consumption Pattern ◽  
Non Linear ◽  
Functional Forms

Abstract Focus of Presentation Research has long found ‘J-shaped’ relationships between alcohol consumption and certain health outcomes, indicating a protective effect of moderate consumption. However, methodological limitations in most studies hinder causal inference. While enhanced methods for data analysis (e.g. G-methods) and alternative observational designs (e.g. Mendelian Randomisation) have been developed, they are not commonly applied to alcohol–health research. This presentation will report on a systematic review of observational studies that employ improved approaches to mitigate confounding in characterising alcohol–long-term health relationships (PROSPERO registration: CRD42020185861). Findings MEDLINE, PsycINFO, Embase and SCOPUS were searched in May 2020, yielding 16 published manuscripts reporting on cancer, diabetes, dementia, mental health, cardiovascular health, mortality, HIV seroconversion, and musculoskeletal health. Study findings were qualitatively synthesised. A variety of functional forms were found, including reverse J/J-shaped relationships for prostate cancer and related mortality, dementia risk, mental health, and certain lipids. However, most outcomes were only evaluated by a single study, and few studies provided information on the role of alcohol consumption pattern. Conclusions/Implications More research employing enhanced causal inference methods is urgently required to accurately characterise alcohol–long-term health relationships. Those studies that have been conducted find a variety of linear and non-linear functional forms, with results tending to be discrepant even within specific health outcomes. Key messages A systematic review found that those studies of alcohol–long-term health relationships employing enhanced causal methods are too few and inconsistent to establish whether non-linear alcohol–health relationships exist.

scholarly journals Are there non-linear relationships between alcohol consumption and long-term health?: a systematic review of observational studies employing approaches to improve causal inference

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Rachel Visontay ◽  
Matthew Sunderland ◽  
Tim Slade ◽  
Jack Wilson ◽  
Louise Mewton
Keyword(s):  
Mental Health ◽  
Alcohol Consumption ◽  
Health Outcomes ◽  
Causal Inference ◽  
Observational Studies ◽  
Consumption Pattern ◽  
Human Populations ◽  
Non Linear ◽  
Functional Forms

Abstract Background Research has long found ‘J-shaped’ relationships between alcohol consumption and certain health outcomes, indicating a protective effect of moderate consumption. However, methodological limitations in most studies hinder causal inference. This review aimed to identify all observational studies employing improved approaches to mitigate confounding in characterizing alcohol–long-term health relationships, and to qualitatively synthesize their findings. Methods Eligible studies met the above description, were longitudinal (with pre-defined exceptions), discretized alcohol consumption, and were conducted with human populations. MEDLINE, PsycINFO, Embase and SCOPUS were searched in May 2020, yielding 16 published manuscripts reporting on cancer, diabetes, dementia, mental health, cardiovascular health, mortality, HIV seroconversion, and musculoskeletal health. Risk of bias of cohort studies was evaluated using the Newcastle-Ottawa Scale, and a recently developed tool was used for Mendelian Randomization studies. Results A variety of functional forms were found, including reverse J/J-shaped relationships for prostate cancer and related mortality, dementia risk, mental health, and certain lipids. However, most outcomes were only evaluated by a single study, and few studies provided information on the role of alcohol consumption pattern. Conclusions More research employing enhanced causal inference methods is urgently required to accurately characterize alcohol–long-term health relationships. Those studies that have been conducted find a variety of linear and non-linear functional forms, with results tending to be discrepant even within specific health outcomes. Trial registration PROSPERO registration number CRD42020185861.

B. The Non-Linear Calculations for Pulsating Stars

1967 ◽  
Vol 28 ◽  
pp. 105-176
Author(s):  
Robert F. Christy
Keyword(s):  
Major Part ◽  
Afternoon Session ◽  
Discussion Session ◽  
Pulsating Stars ◽  
Fourth Symposium ◽  
Non Linear ◽  
Considerable Discussion ◽  
Informal Approach ◽  
Preceding Session

(Ed. note: The custom in these Symposia has been to have a summary-introductory presentation which lasts about 1 to 1.5 hours, during which discussion from the floor is minor and usually directed at technical clarification. The remainder of the session is then devoted to discussion of the whole subject, oriented around the summary-introduction. The preceding session, I-A, at Nice, followed this pattern. Christy suggested that we might experiment in his presentation with a much more informal approach, allowing considerable discussion of the points raised in the summary-introduction during its presentation, with perhaps the entire morning spent in this way, reserving the afternoon session for discussion only. At Varenna, in the Fourth Symposium, several of the summaryintroductory papers presented from the astronomical viewpoint had been so full of concepts unfamiliar to a number of the aerodynamicists-physicists present, that a major part of the following discussion session had been devoted to simply clarifying concepts and then repeating a considerable amount of what had been summarized. So, always looking for alternatives which help to increase the understanding between the different disciplines by introducing clarification of concept as expeditiously as possible, we tried Christy's suggestion. Thus you will find the pattern of the following different from that in session I-A. I am much indebted to Christy for extensive collaboration in editing the resulting combined presentation and discussion. As always, however, I have taken upon myself the responsibility for the final editing, and so all shortcomings are on my head.)

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

Sign in / Sign up

Export Citation Format

Share Document