Conditional independence is a statistical concept that describes a situation where two variables, A and B, are independent of each other given a third variable, C. This means that knowing the value of C provides no additional information about the relationship between A and B. In other words, once we account for C, the behavior of A does not affect the behavior of B and vice versa. This concept is crucial in fields like machine learning and probability theory, as it simplifies the analysis of complex systems. By identifying conditional independence, researchers can reduce the number of variables they need to consider, making models more efficient and easier to interpret.