isixsigma on MSN2d
Causal Loop Diagrams: Little Known Analytical Tool
Key Points Causal loop diagrams are visual aids for analyzing relationships between variables. They can serve as a means of ...
Sacramento State University6y
Causal Arguments and Causal Fallacies
(In other words, it is not a Red Herring to cite temporal succession as part of an argument for a causal conclusion.) We generally understand it as a necessary condition of A causing B that B occur ...
isixsigma on MSN18d
Cause-and-Effect Diagrams: A Visual Tool for Root Cause Analysis
The ability to visually represent many potential causes and explore causal relationships ... and Measurements. An example of ...
ssir.org1y
The Case for Causal AI
by using causal AI algorithms to ask what-if questions. For example, if a specific training program is implemented to improve teacher competency, by how much should we expect student math test scores ...
Psychology Today3d
How Do Synchronicity and Serendipity Differ?
How do you use the terms serendipity and synchronicity? Do they overlap? This post describes the similarities and the ...
Tech Xplore on MSN12d
Revealing causal links in complex systems: New algorithm reveals hidden influences
Getting to the heart of causality is central to understanding the world around us. What causes one variable—be it a ...
BBC1y
Risk factors and causal mechanisms
Scientists have established several causal mechanisms for risk factors. Below is an example of how risk factors have been found. This has come from studies on smoking cigarettes and lung cancer.
A causal theory for studying the cause-and-effect relationships of genes
Researchers developed theoretical foundations for methods that could identify the best way to aggregate genes into modules and efficiently learn the underlying cause-and-effect relationships among ...
University of Bristol1y
Causal Inference in Epidemiology: Concepts and Methods
Causal inferences from observational studies rely on assumptions, some of which we cannot test using the data. Therefore it is important to learn the rules of Directed acyclic graphs (DAGs) as a way ...
Cambridge University Press2y
Causal Inference for Statistics, Social, and Biomedical Sciences
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world ...