The article introduces a method called the possible worlds framework to determine causal relationships between variables, even when some information is missing. By creating possible worlds diagrams, researchers can show all potential observations and prove when causal relationships are not compatible. This framework can also be used to solve the problem of possibilistic causal compatibility. Additionally, graphical symmetries and consistency constraints can be used to create a hierarchy of tests that lead to conclusive results.