The Assumptions of Direction Dependence Analysis
Bibliographic Data
| ID | 19290010 |
|---|---|
| Authors | Felix Thoemmes (0000-0001-5689-2659, Cornell University, corresponding author) |
| Year | 2020 |
| Volume | 55 |
| Issue | 4 |
| Pages | 516-522 |
| Publication date | 2020-07-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2019.1608800 |
| PMID | 31215241 |
| OpenAlex | W2951474692 |
| Language | EN |
| Citations received | 1 |
| References cited | 6 |
Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions
Causal analysis · Context (archaeology) · Data mining · Econometrics · Kurtosis · Machine learning · Regression analysis · Skew · Statistics · Advanced Statistical Modeling Techniques · Computer Science · Face and Expression Recognition · Mathematics · Optimal Experimental Design Methods
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,17 |
| Citation span | 2020 - 2020 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |