Causal Markov

Causal Markov condition research focuses on uncovering causal relationships from observational data, often in the presence of hidden variables or confounding factors, aiming to build accurate causal models. Current research emphasizes developing algorithms, such as iterative causal discovery methods, that efficiently learn causal structures by leveraging conditional independence tests and incorporating constraints derived from the causal Markov condition, even under conditions like latent variables or selection bias. These advancements improve the reliability of causal inference in various fields, enabling more robust analyses in areas like time series analysis, medical science, and experimental design where accurate causal understanding is crucial. The resulting models facilitate better decision-making and more precise predictions by accounting for complex causal relationships.