In probability theory, conditional dependence is a relationship between two or more events that are dependent when a third event occurs. For example, if A and B are two events that individually affect the happening of a third event C, and do not directly affect each other, then initially (when the event C has not occurred)
Eventually the event C occurs, and now if event A occurs the probability of occurrence of the event B will decrease (similarly event B occurring first will decrease the probability of occurrence of A in future). Hence, now the two events A and B become conditionally dependent because their probability of occurrence is dependent on either event's occurrence. Intuitively we can say that since A and B both were probable causes of C, given C has occurred, occurrence of either of A or B alone could explain away the happening of C.
Conditional dependence is different from conditional independence. In conditional independence two events which are initially dependent become independent given the occurrence of a third event.
- Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig, 2011 "Unit 3: Conditional Dependence"
- Introduction to learning Bayesian Networks from Data by Dirk Husmeier  "Introduction to Learning Bayesian Networks from Data -Dirk Husmeier"
- Conditional Independence in Statistical theory "Conditional Independence in Statistical Theory", A. P. Dawid"
- Probabilistic independence on Britannica "Probability->Applications of conditional probability->independence (equation 7) "
- Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig, 2011 "Unit 3: Explaining Away"
- Conditional Independence in Statistical theory "Conditional Independence in Statistical Theory", A. P. Dawid