epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical...

Distinguish between the root cause and other causal factors (e.g., via event correlation) Establish a causal graph between the root cause and the problem....

In mathematics, particularly graph theory, and computer science, a directed acyclic graph (DAG) is a directed graph with no directed cycles. That is, it...

of measurement. While derivations in causal calculus rely on the structure of the causal graph, parts of the causal structure can, under certain assumptions...

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main...

if releasing one's fingers from a hammer always causes it to fall. A causal graph could be created to acknowledge that both the presence of gravity and...

A causal loop diagram (CLD) is a causal diagram that visualizes how different variables in a system are causally interrelated. The diagram consists of...

computer science, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context...

why–because graph (WBG), which is a type of causal notation used to represent interdependencies within a system. The WBG depicts causal relations between...

Acyclic Graphs (DAGs). However the phrase “causal map” is usually reserved for qualitative or merely semi-quantitative maps. In this sense, causal maps can...

disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment...

that causal bar. This results in a causal conflict, the corrected version of the graph is below, with the ⋆ {\textstyle \star } representing the causal conflict...

In statistics and causal graphs, a variable is a collider when it is causally influenced by two or more variables. The name "collider" reflects the fact...

metaphysics, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation...

conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian...

other symbols. Causal notation is notation used to express cause and effect. In nature and human societies, many phenomena have causal relationships where...

In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected...

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four...

representations for the causal context meta-data. One is to maintain an explicit dependency graph of the causal dependence relation. Because such a graph can grow arbitrarily...

to determine whether causal effects can be identified from observational data under specific assumptions encoded in a causal graph. It provides a systematic...

framework to deal with causal analysis of fairness. They suggest the use of a Standard Fairness Model, consisting of a causal graph with 4 types of variables:...

The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the...

In graph theory, an orientation of an undirected graph is an assignment of a direction to each edge, turning the initial graph into a directed graph. A...

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

specifically in graph theory, a polytree (also called directed tree, oriented tree or singly connected network) is a directed acyclic graph whose underlying...

acceptance by several technical disciplines (specifically statistics and formal causal analysis). OpenMx has as its icon a representation of Wright's piebald guinea...

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

causal links on the basis of highlighted passages of source text cauzality.com compendiuminstitute.org dagitty.net banxia.com "GraphCommons". "GraphCommons...

the counterfactual tradition of causal analysis with the variants of structural equation modeling worth keeping. The graph theory that he uses to accomplish...

In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding...