  From simulating galaxy formation to viral transmission in a pandemic,
scientific models play a pivotal role in developing scientific theories and
supporting government policy decisions that affect us all. Given these critical
applications, a poor modelling assumption or bug could have far-reaching
consequences. However, scientific models possess several properties that make
them notoriously difficult to test, including a complex input space, long
execution times, and non-determinism, rendering existing testing techniques
impractical. In fields such as epidemiology, where researchers seek answers to
challenging causal questions, a statistical methodology known as Causal
Inference has addressed similar problems, enabling the inference of causal
conclusions from noisy, biased, and sparse data instead of costly experiments.
This paper introduces the Causal Testing Framework: a framework that uses
Causal Inference techniques to establish causal effects from existing data,
enabling users to conduct software testing activities concerning the effect of
a change, such as Metamorphic Testing, a posteriori. We present three case
studies covering real-world scientific models, demonstrating how the Causal
Testing Framework can infer metamorphic test outcomes from reused, confounded
test data to provide an efficient solution for testing scientific modelling
software.
