scholarly journals Qualitative approximations to causality: Non-randomizable factors in clinical psychology

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Michael Höfler ◽  
Sebastian Trautmann ◽  
Philipp Kanske
Keyword(s):  
Clinical Psychology ◽  
Quantitative Methods ◽  
Quantitative Estimation ◽  
False Negative ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Qualitative Approaches ◽  
Acyclic Graphs ◽  
Negative Conclusion ◽  
Causal Conclusion

Background Causal quests in non-randomized studies are unavoidable just because research questions are beyond doubt causal (e.g., aetiology). Large progress during the last decades has enriched the methodical toolbox. Aims Summary papers mainly focus on quantitative and highly formal methods. With examples from clinical psychology, we show how qualitative approaches can inform on the necessity and feasibility of quantitative analysis and may yet sometimes approximate causal answers. Results Qualitative use is hidden in some quantitative methods. For instance, it may yet suffice to know the direction of bias for a tentative causal conclusion. Counterfactuals clarify what causal effects of changeable factors are, unravel what is required for a causal answer, but do not cover immutable causes like gender. Directed acyclic graphs (DAGs) address causal effects in a broader sense, may give rise to quantitative estimation or indicate that this is premature. Conclusion No method is generally sufficient or necessary. Any causal analysis must ground on qualification and should balance the harms of a false positive and a false negative conclusion in a specific context.

scholarly journals Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

2020 ◽  
Author(s):  
Peter W G Tennant ◽  
Eleanor J Murray ◽  
Kellyn F Arnold ◽  
Laurie Berrie ◽  
Matthew P Fox ◽  
...  
Keyword(s):  
Health Research ◽  
Web Of Science ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Research Review ◽  
Future Research ◽  
Substantial Variation ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Applied Health Research

Abstract Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Methods Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. Results A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). Conclusion There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

scholarly journals Causal Analysis as a Bridge Between Qualitative and Quantitative Research

2021 ◽  
Author(s):  
Rosemary Blersch ◽  
Neil Franchuk ◽  
Miranda Lucas ◽  
Christina Nord ◽  
Stephanie Varsanyi ◽  
...  
Keyword(s):  
Qualitative Methods ◽  
Quantitative Research ◽  
Quantitative Methods ◽  
Causal Analysis ◽  
Directed Acyclic Graphs ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Acyclic Graphs

Yarkoni argues that one solution is to abandon quantitative methods for qualitative ones. While we agree that qualitative methods are under-valued, we argue that both are necessary for thoroughgoing psycholog-ical research, complementing one another through the use of causal analysis. We illustrate how directed acyclic graphs can bridge qualitative and quantitative methods, thereby fostering understanding between dif-ferent psychological methodologies.

Directed Acyclic Graphs: Alternative tool for causal inference in epidemiology and biostatistics research and teaching

MedPharmRes ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 12-16 ◽  
Author(s):  
Dang Tran ◽  
Long Khuong ◽  
Tram Huynh ◽  
Hong Le ◽  
Tuan Vo
Keyword(s):  
Traditional Approach ◽  
Univariate Analysis ◽  
Experimental Studies ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
P Value ◽  
Causal Inferences ◽  
Disease Patterns ◽  
Research And Teaching ◽  
Acyclic Graphs

The issue of causation is one of the major challenges for epidemiologists who aim to understand the association between an exposure and an outcome to explain disease patterns and potentially provide a basis for intervention. Suitably designed experimental studies can offer robust evidence of the causal relationships. The experimental studies, however, are not popular, difficult or even unethical and impossible to conduct; it would be desirable if there is a methodology for reducing bias or strengthening the causal inferences drawn from observational studies. The traditional approach of estimating causal effects in such studies is to adjust for a set of variables judged to be confounders by including them in a multiple regression. However, which variables should be adjusted for as confounders in a regression model has long been a controversial issue in epidemiology. From my observation, the adjustments using only "statistical artifacts" methods such as the p-value<0.2 in univariate analysis, stepwise (forward/backward) are widely used in research and teaching in Epidemiology and Statistics but without appropriated notice on the biological or clinical relationships between exposure and outcome which may induce the bias in estimating causal effects. In this mini-review, we introduce an interesting method, namely Directed Acyclic Graphs (DAGs), which can be used to reduce the bias in estimating causal effects; it is also a good application for Epidemiology and Biostatistics teaching.

scholarly journals Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

2019 ◽  
Author(s):  
Peter WG Tennant ◽  
Wendy J Harrison ◽  
Eleanor J Murray ◽  
Kellyn F Arnold ◽  
Laurie Berrie ◽  
...  
Keyword(s):  
Health Research ◽  
Web Of Science ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Research Review ◽  
Future Research ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Applied Health Research ◽  
Consistent Direction

ABSTRACTBackgroundDirected acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require adjustment when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.MethodsOriginal health research articles published during 1999-2017 mentioning “directed acyclic graphs” or similar or citing DAGitty were identified from Scopus, Web of Science, Medline, and Embase. Data were extracted on the reporting of: estimands, DAGs, and adjustment sets, alongside the characteristics of each article’s largest DAG.ResultsA total of 234 articles were identified that reported using DAGs. A fifth (n=48, 21%) reported their target estimand(s) and half (n=115, 48%) reported the adjustment set(s) implied by their DAG(s).Two-thirds of the articles (n=144, 62%) made at least one DAG available. Diagrams varied in size but averaged 12 nodes (IQR: 9-16, range: 3-28) and 29 arcs (IQR: 19-42, range: 3-99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31-67, range: 12-100). 37% (n=53) of the DAGs included unobserved variables, 17% (n=25) included super-nodes (i.e. nodes containing more than one variable, and a 34% (n=49) were arranged so the constituent arcs flowed in a consistent direction.ConclusionsThere is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlight some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

scholarly journals Structural learning and estimation of joint causal effects among network-dependent variables

2021 ◽  
Author(s):  
Federico Castelletti ◽  
Alessandro Mascaro
Keyword(s):  
Network Structure ◽  
Causal Effect ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Structural Learning ◽  
Acyclic Graphs ◽  
Dependent Variables ◽  
Underlying Network ◽  
Dag Model ◽  
Joint Posterior Distribution

AbstractBayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables, a process known as structural learning. In addition, when equipped with the notion of intervention, a causal DAG model can be adopted to quantify the causal effect on a response due to a hypothetical intervention on some variable. Observational data cannot distinguish between DAGs encoding the same set of conditional independencies (Markov equivalent DAGs), which however can be different from a causal perspective. In addition, because causal effects depend on the underlying network structure, uncertainty around the DAG generating model crucially affects the causal estimation results. We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from simultaneous interventions on any given set of variables in the system. Our approach fully accounts for the uncertainty around both the network structure and causal relationships through a joint posterior distribution over DAGs, DAG parameters and then causal effects.

scholarly journals An Overview of Causal Directed Acyclic Graphs for Substance Abuse Researchers

2017 ◽  
Author(s):  
Michael Lewis ◽  
Alexis Kuerbis
Keyword(s):  
Substance Abuse ◽  
Epidemiological Data ◽  
Experimental Manipulation ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Model Specification ◽  
Design Data ◽  
Logistic Regression Models ◽  
Gateway Hypothesis ◽  
Acyclic Graphs

Background. Within substance abuse research, quantitative methodologists tend to view randomized controlled trials (RCTs) as the “gold standard” for estimating causal effects, in part due to experimental manipulation and random assignment. Such methods are not always possible due to ethical and other reasons. Causal directed acyclic graphs (causal DAGs) are mathematical tools for (1) precisely stating researchers' causal assumptions and (2) providing guidance regarding the specification of statistical models for causal inference with nonexperimental data (such as epidemiological data). Purpose. This manuscript describes causal DAGs and illustrates their use in regards to a long standing theory within the field of substance use: the gateway hypothesis. Design. Data from the 2013 National Survey of Drug Use and Health are utilized to illustrate the application of causal DAGs in model specification. Then using the model specification constructed via causal DAGs, logistic regression models are used to generate odds ratios of the likelihood of trying heroin, given that one has tried alcohol, marijuana, and/or tobacco. Conclusion. Granting the assumptions encoded in specific causal DAGs, researchers, even in the absence of RCTs, can identify and estimate causal effects of interest.

CYTOLOGICAL AND IMMUNOCYTOCHEMICAL DIAGNOSIS OF TUMOR PLEURITIS

2017 ◽  
Vol 63 (6) ◽  
pp. 894-899
Author(s):  
Viktor Novik ◽  
A. Nefedova ◽  
Ye. Yakubo ◽  
O. Ivanov ◽  
Yekaterina Shalina ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Malignant Tumor ◽  
Cytological Study ◽  
False Negative ◽  
Unknown Primary ◽  
Cytological Examination ◽  
Immunocytochemical Studies ◽  
Negative Conclusion ◽  
Primary Localization

Cytological examination of smears from the sediment after centrifugation of pleural fluids was performed in 479 patients who underwent examination and treatment at our institution in 2014-2016. In 249 (52%) patients tumor cells were not detected in smears, in 230 (48%) observations a suspicion (28 observations) or a confident conclusion (202 observations) on the presence of malignant tumor cells in the exudates was cytologically expressed. In 38 cases immunocytochemical studies was additionally performed. In two observations a false-negative conclusion about the absence of tumor cells in smears was expressed. The sensitivity of the cytological study in the diagnosis of malignant pleuritis was 99.0%. Affirmative cytological conclusions on the presence of malignant pleuritis were given in 87.0% of observations, suspicious cytological responses - in 12.0% of cases. Immunocytochemical studies significantly expanded the possibilities of cytological research and were of great importance in the diagnosis of metastases of tumors of unknown primary localization.

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