Direction Dependence Analysis Research Articles

Does the carbon market signal the market efficiency of clean and dirty cryptocurrencies? An analysis of quantile directional dependence

The role of the carbon market in the market efficiency of cryptocurrencies has not received much attention from scholars, despite its particular relevance for integrating environmental externalities into the theory and practice of digital financial markets. We investigate whether and how the performance of the carbon market during busts and booms affects the market efficiency of both clean and dirty cryptocurrencies. Overall, our results underscore the complex quantile dependence of cryptocurrency market efficiency on carbon market performance, highlighting significant variability in how different types of cryptocurrencies respond to carbon market. These results have significant implications for regulatory policies, investment strategies, industry practices, public perceptions, and technological advances in the pursuit of aligning cryptocurrency markets with environmental sustainability goals.

Evaluating the Causal Structure of the Relationship Between Belonging and Academic Self-Efficacy in Community College: An Application of Direction Dependence Analysis

Evaluating the Causal Structure of the Relationship Between Belonging and Academic Self-Efficacy in Community College: An Application of Direction Dependence Analysis

Supplemental Material for One Step at a Time: A Statistical Approach for Distinguishing Mediators, Confounders, and Colliders Using Direction Dependence Analysis

Supplemental Material for One Step at a Time: A Statistical Approach for Distinguishing Mediators, Confounders, and Colliders Using Direction Dependence Analysis

One step at a time: A statistical approach for distinguishing mediators, confounders, and colliders using direction dependence analysis.

In observational data, understanding the causal link when estimating the causal effect of an independent variable (x) on a dependent variable (y) often requires researchers to identify the role of a third variable in the x → y relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions. Commonly used covariance-based statistical methods, such as linear regression and structural equation modeling, cannot distinguish these effects in practice, however. In this study, we introduce a statistical approach for distinguishing mediators, confounders, colliders, and potential M-bias structures that uses higher-order moment information from the data. We propose a two-step procedure that uses the Hilbert-Schmidt independence criterion within the direction dependence analysis framework. Results from Monte Carlo simulations show that our proposed approach accurately recovers the true data-generating process of the third variable. We provide an empirical example to demonstrate the application of our proposed approach in psychological research. Finally, we discuss implications and future directions of our work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Examination of serum metabolome altered by cigarette smoking identifies novel metabolites mediating smoking-BMI association.

The authors hypothesize that an untargeted metabolomics study will identify novel mechanisms underlying smoking-associated weight loss. This study performed cross-sectional analyses among 1,252 participants in the Bogalusa Heart Study and assessed 1,202 plasma metabolites for mediation effects on smoking-BMI associations. Significant metabolites were tested for associations with smoking genetic risk scores among a subset of participants (n=654) with available genomic data, followed by direction dependence analysis to investigate causal relationships between the metabolites and smoking and BMI. All analyses controlled for age, sex, race, education, alcohol drinking, and physical activity. Compared with never smokers, current and former smokers had a 3.31-kg/m2 and 1.77-kg/m2 lower BMI after adjusting for all covariables, respectively. A total of 22 xenobiotics and 94 endogenous metabolites were significantly associated with current smoking. Eight xenobiotics were also associated with former smoking. Forty metabolites mediated the smoking-BMI associations, and five showed causal relationships with both smoking and BMI. These metabolites, including 1-oleoyl-GPE (18:1), 1-linoleoyl-GPE (18:2), 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4), α-ketobutyrate, and 1-palmitoyl-GPE (16:0), mediated 26.0% of the association between current smoking and BMI. This study cataloged plasma metabolites altered by cigarette smoking and identified five metabolites that partially mediated the association between current smoking and BMI.

Conditional Direction of Dependence Modeling: Application and Implementation in SPSS

Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation ( x → y vs. y → x) and potential of confounding ( x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only change means, variances, and covariances, but also the distributional shape (i.e., skewness, kurtosis, co-skewness, and co-kurtosis) of another variable given the moderator. Such distributional changes can be used to study underlying mechanisms of heterogenous causal effects. The present study introduces conditional direction of dependence modeling and presents SPSS macros to make CDDA easily accessible to applied researchers. A real-world data example from the field of gambling addiction research is used to introduce the functionality of CDDA SPSS macros. Limitations of CDDA due to violated assumptions and poor data quality are discussed. The CDDA installation package is available at no charge from www.ddaproject.com .

Prosocial skills causally mediate the relation between effective classroom management and academic competence: An application of direction dependence analysis.

Direction dependence analysis (DDA) is a recently developed method that addresses the need for more sophisticated tools to evaluate causal mechanisms of developmental processes and interventions. The present study applied DDA to evaluate the hypothesized mediators of a classroom behavior management training program on student academic competence. The study involved a group randomized controlled trial with 105 teachers and 1,818 students (K-3rd grade) in a large urban school district in the United States. Analyses revealed only student prosocial skill development causally mediated the intervention's effects on student academic competence. The findings support the importance of explicit instruction and coaching of student social skills as part of classroom behavior management programs and confirm the causal link between prosocial skills and academic success. The findings are discussed with regard to implications for future applications of DDA in developmental research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Supplemental Material for Prosocial Skills Causally Mediate the Relation Between Effective Classroom Management and Academic Competence: An Application of Direction Dependence Analysis

Supplemental Material for Prosocial Skills Causally Mediate the Relation Between Effective Classroom Management and Academic Competence: An Application of Direction Dependence Analysis

Conditional Direction Dependence Analysis: Evaluating the Causal Direction of Effects in Linear Models with Interaction Terms

Direction dependence analysis (DDA) makes use of higher than second moment information of variables (x and y) to detect potential confounding and to probe the causal direction of linear variable relations (i.e., whether x → y or y → x better approximates the underlying causal mechanism). The “true” predictor is assumed to be a continuous nonnormal exogenous variable. Existing methods compatible with DDA, however, are of limited use when the relation of a focal predictor and an outcome is affected by a moderator. This study presents a conditional direction dependence analysis (CDDA) framework which enables researchers to evaluate the causal direction of conditional regression effects. Monte–Carlo simulations were used to evaluate two different moderation scenarios: Study 1 evaluates the performance of CDDA tests when a moderator affects the strength of the causal effect x → y. Study 2 evaluates cases in which the causal direction itself (x → y vs y → x) depends on moderator values. Study 3 evaluates the robustness of DDA tests in the presence of functional model misspecifications. Results suggest that significance tests compatible with CDDA are suitable in both moderation scenarios, i.e., CDDA allows one to discern regions of a moderator in which the causal direction is uniquely identifiable. An empirical example is provided to illustrate the approach.

Sensitivity Analysis and Extensions of Testing the Causal Direction of Dependence: A Rejoinder to Thoemmes (2019)

A commentary by Thoemmes on Wiedermann and Sebastian’s introductory article on Direction Dependence Analysis (DDA) is responded to in the interest of elaborating and extending direction dependence principles to evaluate causal effect directionality. Considering Thoemmes’ observation that some DDA principles are already well-established in machine learning, we argue that several other connections between DDA and research lines in theoretical statistics, econometrics, and quantitative psychology exist, suggesting that DDA is best conceptualized as a framework that summarizes and extends existing knowledge on properties of linear models under non-normality. Further, Thoemmes articulates concerns about assumptions of error distributions used in our main article and presents an artificial data example in which some DDA tests have suboptimal statistical power. We present extensions of DDA to entirely relax distributional assumptions about errors and describe two sensitivity analysis approaches to critically evaluate DDA results. Both sensitivity approaches are illustrated using Thoemmes’ artificial data example. Incorporating DDA sensitivity results yields correct causal conclusions. Thus, overall, we stay with our initial conclusion that the use of higher moments in causal inference constitutes an exciting open research area.

Direction Dependence Analysis in the Presence of Confounders: Applications to Linear Mediation Models Using Observational Data

Statistical methods to identify mis-specifications of linear regression models with respect to the direction of dependence (i.e. whether or better approximates the data-generating mechanism) have received considerable attention. Direction dependence analysis (DDA) constitutes such a statistical tool and makes use of higher-moment information of variables to derive statements concerning directional model mis-specifications in observational data. Previous studies on direction of dependence mainly focused on statistical inference and guidelines for the selection from the two directionally competing candidate models ( versus ) while assuming the absence of unobserved common causes. The present study describes properties of DDA when confounders are present and extends existing DDA methodology by incorporating the confounder model as a possible explanation. We show that all three explanatory models can be uniquely identified under standard DDA assumptions. Further, we discuss the proposed approach in the context of testing competing mediation models and evaluate an organizational model proposing a mediational relation between school leadership and student achievement via school safety using observational data from an urban school district. Overall, DDA provides strong empirical support that school safety has indeed a causal effect on student achievement but suggests that important confounders are present in the school leadership–safety relation.

Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS.

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

ABSTRACTHeteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

Directional Dependence in the Analysis of Single Subjects

Many statistical methods applied in person-oriented research make use of theoretical principles originally derived in a variable-oriented context. From this perspective, it naturally follows that advances originated in variableoriented methodology may potentially contribute to the development of methods suitable for person-oriented perspectives. Direction Dependence Analysis (DDA) constitutes one of these recent advances and provides a framework to statistically evaluate asymmetric properties of observed variable relations. These asymmetric properties enable researchers to make statements whether a model of the form x → y or a model assuming y → x is more likely to approximate the underlying data-generating process in non-experimental settings. The present article introduces DDA to the context of person-oriented research and extends the DDA principle to (linear) vector autoregressive models (VAR) which can be used to describe individual development. We show that DDA can be used to empirically evaluate directional theories of (potentially multivariate) intraindividual development (e.g., which of two longitudinally observed variables is more likely to be the explanatory variable and which one is more likely to reflect the outcome). An illustrative example is provided from a study on the development of experienced mood and alcohol consumption behavior. It is demonstrated that VAR-DDA resolves the issue of identifying the direction of contemporaneous effects in longitudinal data. Temporality issues of directional theories used to explain intraindividual development, guidelines to achieve acceptable power, methodological requirements, and potential further extensions of DDA for person-oriented research are discussed.

Analysis of directional dependence using asymmetric copula-based regression models

The directional dependence between variables using asymmetric copula regression has drawn much attention in recent years. There are, however, some critical issues which have not been properly addressed in regards to the statistical inference of the directional dependence. For example, the previous use of asymmetric copulas failed to fully capture the dependence patterns between variables, and the method used for the parameter estimation was not optimal. In addition, no method was considered for selecting a suitable asymmetric copula or for computing the general measurements of the directional dependence when there are no closed-form expressions. In this paper, we propose a generalized multiple-step procedure for the full inference of the directional dependence in joint behaviour based on the asymmetric copula regression. The proposed procedure utilizes several novel methodologies that have not been considered in the literature of the analysis of directional dependence. The performance and advantages of the proposed procedure are illustrated using two real data examples, one from biological research on histone genes, and the other from developmental research on attention deficit hyperactivity disorder.

Evaluation of structure description requirements for predicting gap fraction of vegetation canopies

Gap fraction in homogeneous crop canopies is usually computed using the Poisson law. For heterogeneous canopies, gap fraction may be computed by assuming the canopy to be made of contiguous cells, in which the Poisson law is used. However, the implicit hypothesis of random phyto-element position is frequently violated, because of plant structure and canopy planting pattern. This may empirically be taken into account by introducing an additional dispersion parameter. However, we are unable, from a deterministic approach, to predict such a parameter. Most experimental works to study the relations between gap fraction and canopy structure suffer from the limited accuracy of the measurements. A laboratory experiment was therefore conducted on artificial canopies, which enabled an accurate description of both structure and gap fraction. The usual turbid medium approach, with random leaf dispersion, but an accurate description of spatial variations of canopy structure, enabled us to predict the directional gap fraction of the artificial canopies with a maximum error of about 0.06. The accuracy of simulations remained fairly good when using simplified descriptions of structure, provided that they took into account most of the spatial variations of leaf area index. The residual error of 0.06 indicates however a significant departure from the Poisson law. Analysis of spatial and directional dependence of gap fraction measurements showed that fitting simulated and experimental data required the introduction of a dispersion parameter incorporating spatial, zenith, and azimuth variations.