Exploratory causal analysis, also known as "data causality" or "causal discovery" [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal …

Jul 24, 2025 · Causal analysis is a process for identifying and addressing the causes and effects of a challenge or problem. Instead of addressing the symptoms of a problem, causal analysis …

Jan 25, 2024 · Causal analysis can offer insightful information when doing research, fixing issues, or making judgments. In this article, we'll break down the concept of causal analysis, step by …

Definition: Causal analysis is a method used to identify the cause of a problem or event. It aims to understand the relationship between different factors and their outcomes.

Nov 14, 2025 · Casual analysis is informal: a quick look at data, gut feelings, or simple charts. Causal analysis is systematic: it uses structured methods and models to uncover cause-and …

May 17, 2022 · For building causal models, we mainly use graph structures. However, it is important to understand certain causal terms before understanding a graph structure. Let us …

Nov 5, 2024 · Welcome to my overview of causal analysis! In this article, we explore the differences among causal inference, experimentation, and causal discovery. I’ve been inspired …

Aug 1, 2023 · Presenting the most important quantitative methods for evaluating causal effects, this textbook provides graduate students and researchers with a clear and comprehensive …

Causal analysis is defined as a statistical method used to investigate the cause-and-effect relationship between a dependent variable and one or more independent variables, …

According to David Hume, causal connections among real events cannot be perceived. Bertrand Russell even suggested that the term causality be banned in scientific discourse. Academic …