Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis...

for causal discovery in 1990. Many recent causal discovery algorithms follow the Spirtes-Glymour approach to verification. Exploratory causal analysis, also...

research Causal research (also referred to as explanatory research) Exploratory research or formulative research: The objective of exploratory research...

Causal layered analysis (CLA) is a future research theory that integrates various epistemic modes, creates spaces for alternative futures, and consists...

components analysis and exploratory factor analysis – Definitions, differences and choices For this reason, Brown (2009) recommends using factor analysis when...

causal determinism, that the occurrence or existence of yet other things depends upon our deliberating, choosing and acting in a certain way. Causal determinism...

latent variables and the indicators. Exploratory and confirmatory factor analysis models, for example, focus on the causal measurement connections, while path...

estimation of threshold parameters. Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are employed to understand shared variance...

previous. This chain of causal dependence may be called a mechanism. Note that the analysis does not purport to explain how we make causal judgements or how...

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...

Systems analysis is "the process of studying a procedure or business to identify its goal and purposes and create systems and procedures that will efficiently...

Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison....

statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the...

limitations that have been alleviated by modern methods of causal analysis, based on causal diagrams and counterfactual logic. The source of these difficulties...

most prominent of these techniques is the analysis of concepts, known as conceptual analysis. While analysis is characteristic of the analytic tradition...

applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of...

on Matrix Analysis and Applications. 30 (3): 1033–1066. doi:10.1137/070690729. Vasilescu, M.A.O.; Kim, E.; Zeng, X.S. (2021), "CausalX: Causal eXplanations...

The Disaggregative Policy Delphi, developed by Petri Tapio, uses cluster analysis as a systematic tool to construct various scenarios of the future in the...

A study of corporate data analysts found two challenges to exploratory time series analysis: discovering the shape of interesting patterns, and finding...

recording before the analysis can take place. Software packages such as SPSS and R are typically used for this purpose. Causal relationships are studied...

Exploratory engineering is a term coined by K. Eric Drexler to describe the process of designing and analyzing detailed hypothetical models of systems...

planning Systems analysis Threatcasting Trend analysis Technology assessment and forecasting Critical design Design fiction Exploratory engineering FTA...

Trend analysis is the widespread practice of collecting information and attempting to spot a pattern. In some fields of study, the term has more formally...

anticipatory methods include, the delphi method, causal layered analysis, environmental scanning, morphological analysis, and scenario planning. The Delphi method...

with scenario planning leads to plausible scenario storylines because the causal relationship between factors can be demonstrated. These cases, in which...

methodology to address vulnerabilities of infrastructure systems. For example, analysis of domestic water infrastructure in the Southern California and Tijuana...

ratio can also be prominently seen in medical diagnostic testing. Exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their...

disentangling the causal factors based on second order or higher order statistics associated with each causal factor. Tensor (multilinear) factor analysis disentangles...

a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two...

a data exploratory technique. Although WGCNA incorporates traditional data exploratory techniques, its intuitive network language and analysis framework...