Fault injection is a technique to measure the robustness of a program to errors by introducing faults into the program under test. Following a fault injection experiment, Error Propagation Analysis (EPA) is deployed to understand how errors affect a program's execution. EPA typically compares the traces of a fault-free (golden) run with those from a faulty run of the program. While this suffices for deterministic programs, EPA approaches are unsound for multithreaded programs with non-deterministic golden runs. In this paper, we propose Invariant Propagation Analysis (IPA) as the use of automatically inferred likely invariants ("invariants" in the following) in lieu of golden traces for conducting EPA in multithreaded programs. We evaluate the stability and fault coverage of invariants derived by IPA through fault injection experiments across six different fault types and six representative programs that can be executed with varying numbers of threads. We find that stable invariants can be inferred in all cases, but their fault coverage depends on the application and the fault type. We also find that fault coverage for multithreaded executions with IPA can be even higher than for traditional singlethreaded EPA, which emphasizes that IPA results cannot be trivially extrapolated from traditional EPA results.