Bi-Directional Causal Analysis using a Novel Coefficient of Causation
T. Dhilip *
Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India.
Ramesh Chandra Bagadi
Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India and Department of Civil Engineering, NSR Institute of Technology, Sontyam, Visakhapatnam, A.P, India.
M. Gopal Naik
Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India.
Suresh Kumar N
Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
The understanding and computation of both correlation and causation are of prime importance to infer relationships between the variables under consideration. Often made conclusions related to causation are subjective and are likely to vary across space and time. The current research investigation aims to develop a model for computing the causal impact of one variable upon another using a Novel Coefficient of Causation. The proposed coefficient involves computing the inner product of two specially transformed variables between which the coefficient of causation is computed. The variable sets considered for the analysis are the Anscombe’s Quartet datasets. It is found that the coefficient well explains the causal behavior between two variables and the same is illustrated through analysis carried out on the standard functions. The proposed model can effectively help in drawing the causal inference objectively. Further, the model also helps in understanding the strength and direction of the causation.
Keywords: Coefficient of causation, bi-directional causal analysis, anscombe’s quartet, MATLAB
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References
Brulé J. A causation coefficient and taxonomy of correlation/causation relationships. 2017;1–31. [Online]. Available: http://arxiv.org/abs/1708.05069.
Singla S, Wallace S, Triantafillou S, Batmanghelich K. Using causal analysis for conceptual deep learning explanation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2021;519–528.
Sarsenbayeva Z, et al. Does smartphone use drive our emotions or vice versa? A causal analysis. In Proceedings of the 2020 CHI conference on human factors in computing systems. 2020;1–15.
Pearson K. Notes on the history of correlation. Biometrika. 1920;13(1):25–45.
Scheines R. “Causation. New Dictrionary Hist. Ideas. 2004;1:1–20.
Spinoza B. 1677/1949 Ethics. Trans. J. Gutman. New York Hafner Publ. Co.
Lopez-Paz D, Hennig P, Schölkopf B. “The randomized dependence coefficient. Adv. Neural Inf. Process. Syst. 2013;26:1–9.
Szabó Z, Gretton A, Póczos B, Sriperumbudur B. Two-stage sampled learning theory on distributions,” in Artificial Intelligence and Statistics. 2015;948–957.
Lopez-Paz D, Muandet K, Schölkopf B, Tolstikhin I. Towards a learning theory of cause-effect inference. In International Conference on Machine Learning. 2015;1452–1461.
Grace JB, Irvine KM. Scientist’s guide to developing explanatory statistical models using causal analysis principles. Ecology. 2020;101(4):e02962.
Sutthichaimethee P, Dockthaisong B. A relationship of causal factors in the economic, social, and environmental aspects affecting the implementation of sustainability policy in Thailand: Enriching the path analysis based on a GMM model,” Resources. 2018;7:4. DOI: 10.3390/resources7040087.
Anscombe FJ. Graphs in statistical analysis. Am. Stat. 1973;27:17–21. [Online]. Available:http://links.jstor.org/sici?sici=0003-1305%28197302%2927%3A1%3C17%3AGISA%3E2.0.CO%3B2-J.