Exploratory Causal Analysis (ECA) Algorithm

AKA: Data Causality Algorithm, Causal Discovery Algorithm.
Context:
- It can be implemented by an Exploratory Causal Analysis System to solve an Exploratory Causal Analysis Task.
- It can range between being a Bivariate Exploratory Causal Analysis Algorithm to being a Multivariate Exploratory Causal Analysis Algorithm.
Example(s):
Counter-Example(s):
- Counterfactual Inference Algorithm.
- Statistical Inference Algorithm.
See: Data Analysis, Causal Analysis, Experimental Design, Statistics, Algorithms, Causal Inference, Randomized Controlled Trials, Exploratory Research.

References

(Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Exploratory_causal_analysis Retrieved:2021-9-24.
- Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery^[1] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials.^[2] It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis^[3]

↑ Rohlfing, Ingo; Schneider, Carsten Q. (2018). "A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research" (PDF). Sociological Methods & Research. 47 (1): 37–63. doi:10.1177/0049124115626170. Retrieved 29 February 2020.
↑ Rosenbaum, Paul (2017). Observation and Experiment: An Introduction to Causal Inference. Harvard University Press. ISBN 9780674975576.
↑ Rosenbaum, Paul (2017). Observation and Experiment: An Introduction to Causal Inference. Harvard University Press. ISBN 9780674975576.

(Scholarpedia, 2021) ⇒ http://www.scholarpedia.org/article/Granger_causality Retrieved:2021-9-24.
- QUOTE: Granger causality is a statistical concept of causality that is based on prediction. According to Granger causality, if a signal X₁ “Granger-causes" (or “G-causes") a signal X₂, then past values of X₁ should contain information that helps predict X₂ above and beyond the information contained in past values of X₂ alone. Its mathematical formulation is based on linear regression modeling of stochastic processes (Granger 1969). More complex extensions to nonlinear cases exist, however these extensions are often more difficult to apply in practice.
  Granger causality (or “G-causality") was developed in 1960s and has been widely used in economics since the 1960s. However it is only within the last few years that applications in neuroscience have become popular.

(Cheek et al., 2018) ⇒ Camden Cheek, Huiyong Zheng, Brian R Hallstrom, and Richard E. Hughes (2018). "Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data". In: Biomedical engineering and computational biology, 9, 1179597218756896.
- QUOTE: The PC causal discovery algorithm of Spirtes et al. (2000) was used to construct DAGs, which are graphs indicating causal structure, from the simulated data. Each node is a variable, and for each pair of nodes (A, B) there are 5 options for connection: (1) no edge indicating that there is no direct causal relationship, (2) a directed edge from A to B indicating that A directly causes B, (3) a directed edge from B to A indicating that B directly causes A, (4) an undirected edge indicating that there is a causal relationship between A and B but the direction cannot be determined from the data available, and (5) there may be a latent variable — or variables — confounding the relationship between A and B. Note that if there is a directed path from A to B, then A can be an indirect cause of B.

(Shimizu, 2014) ⇒ Shohei Shimizu (2014). “Shimizu, S. (2014). "LiNGAM: Non-Gaussian Methods for Estimating Causal Structures". In: Springer- Behaviormetrika, 41(1), 65-98.