Abstract
The application of causal graphs is a useful tool to visualize the relationship between variables and allows the identification of causal and non-causal effects. If the strict rules of the DAG theory are followed, then it is possible to identify confounding and other sources of bias. In this article we show the backtracking algorithm to find all paths of a directed acyclic graph (DAG). The knowledge of the paths can be used to identify systematically all minimally sufficient adjustment sets. The search follows formal rules and can be done by a computer program. The adjacency list and adjacency matrix, which can be used as input for a computer program, are 2 representational forms of a causal graph.
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