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     Direction Dependence Analysis

 

News:

 

  • (March, 2025) New R package for DDA has been released no CRAN (click here for further details).

 

  • (August, 2024) New article on applying DDA: "Shared micromobility, perceived accessibility, and social capital" published in Transportation (click here for the article). 

 

  • (August, 2024) New article on applying DDA: "Sleep quality and the cortisol and alpha-amylase awakening responses in adolescents with depressive disorders" published in BJPsych Open (click here for the article).

 

  • (April, 2024) New article on applying DDA: "Evaluating the causal structure of the relationship between belonging and academic self-efficacy in community college: An application of Direction Dependence Analysis" published in Innovative Higher Education (click here for the article).

 

  • (October, 2023) New article on DDA in the presence of causal effect heterogeneity: "Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach" published in Behavior Research Methods (click here for the article).

 

  • (September, 2023) New article on non-Gaussian covariate selection and covariate-based DDA: "Covariate selection in causal learning under non-Gaussianity" published Behavior Research Methods (click here for the article). 

 

  • (August, 2023) New article on applying DDA: "Learning from bad peers? Influences of peer deviant behaviour on adolescent academic performance" published in the International Journal of Adolescence and Youth (click here for the article).

 

  • (July, 2023) New article on applying DDA: "Interplay between language and mathematics comprehension/learning: A Direction Dependence Analysis" published in the International Journal of Education & Literacy Studies (click here for the article).

 

  • (May, 2023) New article on applying DDA: "The influence of stressful life events on procrastination among college students: multiple mediating roles of stress beliefs and core self-evaluations" published in Frontiers in Psychology (click here for the article).

 

  • (April, 2023) New article on applying DDA: "Television, authoritarianism, and support for Trump: A replication" published in Public Opinion Quarterly (click here for the article).

 

  • (February, 2023) New article on applying DDA in mediation modeling: "Risk perception and graditude mediate the negative relationship between COVID-19 management satisfaction and public anxiety" published in Scientific Reports (click here for the article).

 

  • (October, 2022) New article on applying DDA in mediation modeling: "What drives the perceived prejudice asymmetry among advantaged group members? The mediating role of social group power and moral obligations" published in the European Journal of Social Psychology (click here for the article).

 

  • (March, 2022) New study protocol using DDA "Sleep quality and neurohormonal and psychophysiological accompanying factors in adolescents with depressive disorders: study protocol" published in BJPsych Open (click here for the article).

 

  • (March, 2022) New article on applying DDA in mediation modeling: "Examination of serum metabolome altered by cigarette smoking identifies novel metabolites mediating smoking-BMI association" published in Obesity (click here for the article). 

 

  • (March, 2022) New article on software implementation of Conditional Direction Dependence Analysis (CDDA) in SPSS published in Social Science Computer Review (click here for the article). 

 

  • (February, 2022) New article on applying DDA "Positive teacher-student relationships may lead to better teaching" published in Learning and Instruction (click here for the article). 

 

  • (February, 2022) New invited article on causal inference using third higher moments: "Third moment-based causal inference" published in Behaviormetrika (click here for the article).

 

News Repository...

 

 

 

Workshops/Presentations:

 

  • Columbia, MO, April 24, 2024: "On the problem of the wet toothbrush: Causal structure learning in observational data". Paper presented as the 2nd AI Forum of the University of Missouri, Columbia.

 

  • Washington D.C., August 3-5, 2023 : "Analyzing the Direction Dependence across Subgroups when Competing Causal Theories are Supported". 

 

  • Hikone, Japan, September 10-11, 2021: "Direction Dependence Analysis: A Statistical Framework to Test Cause-Effect Asymmetry (or How to Retire the Mantra "Correlation ≠ Causation" in the Social Sciences). Paper presented online at the International Symposium on Causal Inference and Machine Learning, Shiga University and Behaviormetric Society supported by the RIKEN Center for Advanced Intelligence Project (API).  

 

  • Arlington, VA, September 26-29, 2021: "Variability of Effect Directionality across Subgroups: Examining Causal Assumptions in Moderation Analysis in Non-experimental Design".

 

  • College Park, MA, January 25, 2021: "Direction Dependence Analysis: A Statistical Framework to Test the Causal Direction of Effects in Observational Data". Paper presented online at the Monday Symposium in Measurement and Statistics (MSMS) University of Maryland together with Ohio State University, University of North Carolina at Chapel Hill, and University of Notre Dame.

 

  • Washington, D.C., August 6-8, 2020 (Annual American Psychological Association (APA) Convention): "Predictor-Residual Independence Tests for Examining Causal Assumptions of Synchronous Effects Models". Paper presented at the Symposium "Advanced Quantitative Methods for Addressing Complexities in Psychological Research".

 

  • Arlington, VA, March 11-14, 2020 (Spring 2020 Conference of the Society for Research on Educational Effectiveness; SREE): "Examining the Causal Direction of Synchronous Effects In Structural Panel Models: Using Residual-Predictor Independence Tests to Identify Model Mis-specifications". Paper presented at the Symposium "Considerations and Choices in Modeling Complex Data".

 

  • Lausanne, Switzerland, September 10-12, 2019 (31st Conference of the Austo-Swiss Region (ROeS) of the International Biometric Society): "Using Higher Moments to Test Requirements for Causal Inference". 

 

  • Basel, Switzerland, July 9-12, 2019 (Conference of the International School Psychology Association): "Prosocial Skills Causally Mediate the Relation Between Effective Classroom Management and Academic Competence: An Application of Direction Dependence Analysis". Paper presented at the Symposium "Specifying Mediators of Classroom Social-Behavioral Interventions".

 

  • Chicago, IL, May, 26, 2019 (45th Annual Convention of the Association for Behavior Analysis International): "Direction Dependence Analysis: Testing the Direction of Causation in Non-Experimental Person-Oriented Research". Paper presented at the B. F. Skinner Lecture Series.

 

  • Vancouver, BC, August 1, 2018 (Joint Statistical Meeting): "Direction Dependence Modeling: A Diagnostic Framework to test the Causal Direction of Effects in Linear Models". Paper presented at the Symposium "Recent Development in the Assessment and Modeling of Asymmetric Dependence".
 
  • San Francisco, CA, May 24-27, 2018 (30th APS Annual Convention): "Conditional Direction Dependence Analysis: A Framework to Examine the Direction of Effect in Moderation with Implementation in SPSS".

 

What is Direction Dependence Analysis?

 
A common problem with observational research is that it is often difficult to prove that a specific action causes an effect. Further, in many cases, alternative causal theories exist which may serve as an explanation for observed variable associations. In non-experimental studies, at least three possible explanations exist for the association of two variables x and y:
 
I)    x is the cause of y (i.e., x → y),
II)   y is the cause of x (i.e., y → x),
III) an unmeasured confounder u is present (i.e., x ← u → y).
 
Direction Dependence Analysis (DDA) can be used to uniquely identify each explanatory model. DDA assumes that variables are non-normally distributed and makes use of higher moments (i.e., skewness and kurtosis) to gain deeper insight into the data-generating mechanism. DDA consists of three core components:
 
  • Distributional properties of observed variables
  • Distributional properties of error terms of competing models
  • Independence properties of predictors and error terms of competing models
 
DDA component patterns can be used to uniquely distinguish between the three explanatory models. DDA patterns are summarized below (rectangles represent observed variables and circles represent unobserved variables). Statistical inference methods for model selection, SPSS macros, and R scripts to implement DDA are provided here. Please go to the RESOURCES section to download source codes, manuals, posters, slides, and further information.

 

 

DDA