A Note on Causation versus Correlation in an Extreme Situation

Liang, X. San; Yang, Xiu‐Qun

doi:10.3390/e23030316

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…For nonlinear systems, the existence of such a relation, and, if existing, how it is like, are yet to be explored. Nonetheless, as proved in [25], this relation indeed holds for some counter-examples in terms of normalized information flow (see Section 4 below).…”

Section: Theoremsupporting

confidence: 52%

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Liang

2021

Entropy

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Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

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Section: Theoremsupporting

confidence: 52%

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Liang

2021

Entropy

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“…Correlation does not necessarily indicate causation (Liang & Yang, 2021). Correlation refers to the statistical relationship between two variables, while causation refers to the relationship in which one variable causes another variable to change (Bhandari, 2023).…”

Section: Discussionmentioning

confidence: 99%

The Correlation Between Students’ Reading Habits And Essay Writing Ability In EFL

Abdul Syahid,

Muhammad Noor Fauzan,

Muliana Setia Hapsari

et al. 2023

Jurnal Pendidikan dan Sastra Inggris

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This correlational study explains the relationship between reading habits and essay writing abilities in 3rd semester students in the Essay Writing course. This research, which was conducted at a state Islamic religious institution in Indonesia, used a purposive sampling technique for English Language Education students using a quantitative approach using tests and evaluations based on essay rubrics from the international reading association NCTE for data collection. The normality test shows a non-normal distribution of the reading and writing variables. Shows a weak negative correlation between reading habits and essay writing ability. This discussion emphasizes the importance of understanding the different relationships between these variables, challenging common beliefs and encouraging further exploration in this area. These findings contribute to existing knowledge, indicating that in this study, reading habits did not have a significant effect on essay writing ability.

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“…Measurement errors in variables (Wooldridge, 2010). Correlations among variables do not imply causality for inferences, but causality can imply correlation (Liang and Yang, 2021).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Board diversity and intellectual capital disclosure of Chinese-listed firms with Belt and Road Initiative projects

Zhao,

Abeysekera

2023

JIC

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PurposeChinese-listed firms with Belt and Road Initiatives (BRI) play a crucial role in advancing the outward investment policy of China. Board diversity can be vital, and intellectual capital disclosure (ICD) showing future earnings can build investor confidence in these firms. This study examines these two relationships in Chinese-listed firms with BRI projects during a predictable business outlook period (2019, pre-Covid period) and unpredictable business outlook period (2020, Covid period).Design/methodology/approachThe study used least squares regression that analysed the target population comprising 79 listed Chinese firms with BRI projects in 2019 and 2020. The China Stock Market and Accounting Research (CSMAR) database provided board diversity data. Analysing annual reports using content analysis provided the ICD data, collected by following an established intellectual capital (IC) coding framework in the literature. After collecting board-related data, the study calculated the diversity between boards in firms (diversity of boards – DOB) using cluster analysis. The study estimated the diversity within each board (diversity in boards – DIB) using Blau's Index.FindingsThe findings indicate that in the predictable business outlook environment, DOB positively associates with ICD, and DIB negatively associates with ICD. In the unpredictable business outlook environment, the DIB and DOB interaction negatively associates with ICD, and DOB positively associates with ICD.Research limitations/implicationsThe findings apply to Chinese-listed firms with BRI projects and further research is required to generalise findings beyond them. This study used annual reports to collect ICD, but a future study could examine BRI firms' social media and website disclosures. The attributes selected for board diversity dimensions can contribute to bounded findings, and future studies could expand the board diversity attributes included.Practical implicationsThe findings provide insights into firms' board composition and structure associated with ICD.Originality/valueThis is one of the first studies providing empirical evidence about board diversity and ICD of Chinese-listed firms with BRI projects.

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A Note on Causation versus Correlation in an Extreme Situation

Cited by 10 publications

References 13 publications

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

The Correlation Between Students’ Reading Habits And Essay Writing Ability In EFL

Board diversity and intellectual capital disclosure of Chinese-listed firms with Belt and Road Initiative projects

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