Confusing Correlation with Causation

2019 ◽  
pp. 155-172
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
No Value ◽  
Expert Opinion ◽  
Predictive Value ◽  
Natural Experiment ◽  
Historical Data ◽  
Controlled Experiment ◽  
Relative Importance ◽  
Statistical Relationship ◽  
Causal Relationships

There is a hierarchy of predictive value that can be extracted from data. At the top of the hierarchy are causal relationships that can be confirmed with a randomized and controlled experiment or a natural experiment. Next best is to establish known or hypothesized relationships ahead of time and then test them and estimate their relative importance. One notch lower are associations found in historical data that are tested on fresh data after considering whether or not they make sense. At the bottom of the hierarchy, with little or no value, are associations found in historical data that are not confirmed by expert opinion or tested with fresh data. Data scientists who use a “correlations are enough” approach should remember that the more data and the more searches, the more likely it is that a discovered statistical relationship is coincidental and useless.

scholarly journals Cardio-ventilatory coupling in young healthy resting subjects

2012 ◽  
Vol 112 (8) ◽  
pp. 1248-1257 ◽  
Author(s):  
Lee Friedman ◽  
Thomas E. Dick ◽  
Frank J. Jacono ◽  
Kenneth A. Loparo ◽  
Amir Yeganeh ◽  
...  
Keyword(s):  
Body Surface ◽  
Relative Importance ◽  
Statistical Relationship ◽  
Sinus Arrhythmia ◽  
Retest Reliability ◽  
Respiratory Events ◽  
Before And After ◽  
Test Retest Reliability ◽  
Degree Of Coupling ◽  
High Test

In this work, cardio-ventilatory coupling (CVC) refers to the statistical relationship between the onset of either inspiration (I) or expiration (E) and the timing of heartbeats (R-waves) before and after these respiratory events. CVC was assessed in healthy, young (<45 yr), resting, supine subjects ( n = 19). Four intervals were analyzed: time from I-onset to both the prior R-wave (R-to-I) and the following R-wave (I-to-R), as well as time from E-onset to both the prior R-wave (R-to-E) and following R-wave (E-to-R). The degree of coupling was quantified in terms of transformed relative Shannon entropy (tRSE), and χ2 tests based on histograms of interval times from 200 breaths. Subjects were studied twice, from 5 to 27 days apart, and the test-retest reliability of CVC measures was computed. Several factors pointed to the relative importance of the R-to-I interval compared with other intervals. Coupling was significantly stronger for the R-to-I interval, coupling reliability was largest for the R-to-I interval, and only tRSE for the R-to-I interval was correlated with height, weight, and body surface area. The high test-retest reliability for CVC in the R-to-I interval provides support for the hypothesis that CVC strength is a subject trait. Across subjects, a peak ∼138 ms prior to I-onset was characteristic of CVC in the R-to-I interval, although individual subjects also had earlier peaks (longer R-to-I intervals). CVC for the R-to-I interval was unrelated to two separate measures of respiratory sinus arrhythmia (RSA), suggesting that these two forms of coupling (CVC and RSA) are independent.

Climate and management effects on tick-game animal dynamics.

2021 ◽  
pp. 132-138
Author(s):  
Andrew Hoodless ◽  
Rufus Sage
Keyword(s):  
Climate Change ◽  
Expert Opinion ◽  
Relative Importance ◽  
Host Animal ◽  
Game Animal ◽  
Animal Management ◽  
Contact Rates ◽  
Management Effects

Abstract This expert opinion discusses the mechanisms by which climate and management could alter tick and game host (animal) densities and contact rates, as well as considering the likely relative importance of climate change and game (animal) management in affecting the distribution of ticks in Europe.

An Empirical Assessment of the Comparative Advantage Gains from Trade: Evidence from Japan

2005 ◽  
Vol 95 (1) ◽  
pp. 208-225 ◽  
Author(s):  
Daniel M Bernhofen ◽  
John C Brown
Keyword(s):  
Comparative Advantage ◽  
Natural Experiment ◽  
Historical Data ◽  
Real Income ◽  
Upper Bounds ◽  
Trade Flows ◽  
Gains From Trade ◽  
Empirical Assessment ◽  
Real Gdp ◽  
Opening Up

We provide an empirical assessment of the comparative advantage gains from trade argument. We use Japan’s nineteenth-century opening up to world commerce as a natural experiment to answer the following counterfactual: “By how much would real income have had to increase in Japan during its final autarky years of 1851–1853 to afford the consumption bundle the economy could have obtained if it were engaged in international trade during that period?” Using detailed historical data on trade flows, autarky prices, and Japan’s real GDP, we obtain upper bounds on the gains from trade of about 8 to 9 percent of Japan’s GDP.

scholarly journals Coping, Depression, Anxiety, Self-Efficacy and Social Support: Impact on Adjustment to Chronic Pain

2009 ◽  
Vol 2 (3) ◽  
pp. 8-17
Author(s):  
Maria Alexandra Ferreira Valente ◽  
José Luís Pais Ribeiro ◽  
Mark P. Jensen
Keyword(s):  
Social Support ◽  
Chronic Pain ◽  
Psychosocial Factors ◽  
Self Efficacy ◽  
Psychosocial Interventions ◽  
Relative Importance ◽  
Causal Relationships ◽  
Depression And Anxiety ◽  
Psychosocial Variables ◽  
Coping Responses

Pain is a multidimensional, unique, and private experience. Contemporary biopsychosocial models of chronic pain hypothesize a key role for psychosocial factors as contributing to the experience of and adjustment to chronic pain. The psychosocial factors that have been most often examined as they relate to chronic pain include coping responses, attributions (such as self-efficacy), mood (including depression and anxiety), and social support. Knowledge concerning the relative importance of each of these factors to adjustment is necessary for understanding and developing effective psychosocial interventions. This article reviews the literature concerning the associations between psychosocial factors and adjustment to chronic pain, with a focus on coping, attributions, mood, and social support. Overall, the findings of this research are consistent with biopsychosocial models of chronic pain, and support continued research to help identify the causal relationships among key psychosocial variables and adjustment.

The Natural Controlled Experiment in Organizational Research

1970 ◽  
Vol 29 (3) ◽  
pp. 197-203 ◽  
Author(s):  
Paul Goodman
Keyword(s):  
Research Design ◽  
Laboratory Experiments ◽  
Field Experiments ◽  
Controlled Experiment ◽  
Causal Relationships ◽  
Organizational Research ◽  
Promising Alternative ◽  
Social Organizations ◽  
Controlled Conditions

The purpose of this paper is to delineate a research design-the natural controlled experiment - which offers a promising alternative in studying structure, process and change in social organizations. As the label - "natural controlled experiment" - implies, this design permits evaluations of causal relationships under controlled conditions, in a setting perceived as phenomenologically real (natural by the participants). In one sense this design may be conceptualized as combining some attributes of laboratory experiments and of field experiments of social organizations.

scholarly journals Role of in-phase and out-of-phase chemical shift MRI in differentiation of non-neoplastic versus neoplastic benign and malignant marrow lesions

2021 ◽  
Vol 94 (1119) ◽  
pp. 20200710
Author(s):  
Niels van Vucht ◽  
Rodney Santiago ◽  
Ian Pressney ◽  
Asif Saifuddin
Keyword(s):  
Chemical Shift ◽  
No Value ◽  
Predictive Value ◽  
Bone Tumours ◽  
Imaging Features ◽  
Malignant Bone Tumours ◽  
Neoplastic Lesions ◽  
Chemical Shift Mri ◽  
Sensitivity Specificity ◽  
Benign Bone Tumours

Objective: To determine its ability of in-phase (IP) and out-of-phase (OOP) chemical shift imaging (CSI) to distinguish non-neoplastic marrow lesions, benign bone tumours and malignant bone tumours. Methods: CSI was introduced into our musculoskeletal tumour protocol in May 2018 to aid in characterisation of suspected bone tumours. The % signal intensity (SI) drop between IP and OOP sequences was calculated and compared to the final lesion diagnosis, which was classified as non-neoplastic (NN), benign neoplastic (BN) or malignant neoplastic (MN). Results: The study included 174 patients (84 males; 90 females: mean age 44.2 years, range 2–87 years). Based on either imaging features (n = 105) or histology (n = 69), 44 lesions (25.3%) were classified as NN, 66 (37.9%) as BN and 64 (36.8%) as MN. Mean % SI drop on OOP for NN lesions was 36.6%, for BN 3.19% and for MN 3.24% (p < 0.001). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of CSI for differentiating NN from neoplastic lesions were 65.9%, 94.6%, 80.6%, 89.1%% and 87.4% respectively, and for differentiating BN from MN were 9.1%, 98.4%, 85.7%, 51.2 and 53.1% respectively. Conclusion: CSI is accurate for differentiating non-neoplastic and neoplastic marrow lesions, but is of no value in differentiating malignant bone tumours from non-fat containing benign bone tumours. Advances in knowledge: CSI is of value for differentiating non-neoplastic marrow lesions from neoplastic lesions, but not for differentiating benign bone tumours from malignant bone tumours as has been previously reported.

scholarly journals The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gudrún Höskuldsdóttir ◽  
My Engström ◽  
Araz Rawshani ◽  
Ville Wallenius ◽  
Frida Lenér ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Surgical Treatment ◽  
Psychological Health ◽  
Predictive Value ◽  
Demographic Data ◽  
Gastrointestinal Symptoms ◽  
Obesity Treatment ◽  
Relative Importance ◽  
Lifestyle Habits ◽  
Clinical Criteria

Abstract Background The development of obesity is most likely due to a combination of biological and environmental factors some of which might still be unidentified. We used a machine learning technique to examine the relative importance of more than 100 clinical variables as predictors for BMI. Methods BASUN is a prospective non-randomized cohort study of 971 individuals that received medical or surgical treatment (treatment choice was based on patient’s preferences and clinical criteria, not randomization) for obesity in the Västra Götaland county in Sweden between 2015 and 2017 with planned follow-up for 10 years. This study includes demographic data, BMI, blood tests, and questionnaires before obesity treatment that cover three main areas: gastrointestinal symptoms and eating habits, physical activity and quality of life, and psychological health. We used random forest, with conditional variable importance, to study the relative importance of roughly 100 predictors of BMI, covering 15 domains. We quantified the predictive value of each individual predictor, as well as each domain. Results The participants received medical (n = 382) or surgical treatment for obesity (Roux-en-Y gastric bypass, n = 388; sleeve gastrectomy, n = 201). There were minor differences between these groups before treatment with regard to anthropometrics, laboratory measures and results from questionnaires. The 10 individual variables with the strongest predictive value, in order of decreasing strength, were country of birth, marital status, sex, calcium levels, age, levels of TSH and HbA1c, AUDIT score, BE tendencies according to QEWPR, and TG levels. The strongest domains predicting BMI were: Socioeconomic status, Demographics, Biomarkers (notably TSH), Lifestyle/habits, Biomarkers for cardiovascular disease and diabetes, and Potential anxiety and depression. Conclusions Lifestyle, habits, age, sex and socioeconomic status are some of the strongest predictors for BMI levels. Potential anxiety and / or depression and other characteristics captured using questionnaires have strong predictive value. These results confirm previously suggested associations and advocate prospective studies to examine the value of better characterization of patients eligible for obesity treatment, and consequently to evaluate the treatment effects in groups of patients. Trial registration March 03, 2015; NCT03152617.

Does Quality Win? Network Effects versus Quality in High-Tech Markets

2009 ◽  
Vol 46 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Gerard J. Tellis ◽  
Eden Yin ◽  
Rakesh Niraj
Keyword(s):  
Empirical Analysis ◽  
Market Share ◽  
Network Effects ◽  
Historical Data ◽  
The Other ◽  
High Tech ◽  
Relative Importance ◽  
Leadership Changes ◽  
The One ◽  
Positive Effect

Researchers disagree about the critical drivers of success in and efficiency of high-tech markets. On the one hand, some researchers assert that high-tech markets are efficient with best-quality brands being dominant. On the other hand, many scholars suspect that network effects lead to perverse markets in which the dominant brands do not have the best quality. The authors develop scenarios about the relative importance of these effects and the efficiency of markets. Empirical analysis of historical data on 19 categories shows that though both quality and network effects affect market share flows, in general markets are efficient. In particular, market share leadership changes often, switches in share leadership closely follow switches in quality leadership, and the best-quality brands, not the ones that are first to enter, dominate the market. Network effects enhance the positive effect of quality.

Prediction of Consumer Innovators: Application of Multiple Discriminant Analysis

1968 ◽  
Vol 5 (1) ◽  
pp. 64-69 ◽  
Author(s):  
Thomas S. Robertson ◽  
James N. Kennedy
Keyword(s):  
Discriminant Analysis ◽  
Innovation Diffusion ◽  
Predictive Value ◽  
Social System ◽  
Predictive Ability ◽  
Relative Importance ◽  
Multiple Discriminant Analysis ◽  
Socioeconomic Characteristics ◽  
Analysis Techniques

Socioeconomic characteristics of consumer applicance innovators and non-innovators within a defined social system are assessed. Such characteristics are derived from the innovation-diffusion literature and represent variables of highest predictive ability in previous research. The relative importance of each characteristic and the predictive value of the set of characteristics are measured with multiple discriminant analysis techniques.

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