In legal research, it is often necessary to determine which cases have been influenced by a particular precedent. This can involve tracing not just direct citations but also indirect ones—cases that cite a precedent through a chain of intermediate cases. To accomplish this in Python, you can use either recursion or iteration to traverse a citation network represented as a dictionary. In this context, a dictionary maps each case ID to a list of case IDs it cites as precedents.

To find all cases that are influenced by a given precedent, you need to examine each case in the dictionary and check if it cites the specified precedent, either directly or through a series of intermediate citations. This process is similar to searching through a graph, where cases are nodes and citations are edges. By systematically exploring each case's citations—either by recursively checking if a case's citations eventually lead to the target precedent, or by using an explicit stack or queue for iteration—you can build a set of all cases that are connected to the precedent, directly or indirectly. This approach ensures you identify every case that is part of the citation network rooted at the precedent in question.