  As machine learning systems become more powerful they also become
increasingly unpredictable and opaque. Yet, finding human-understandable
explanations of how they work is essential for their safe deployment. This
technical report illustrates a methodology for investigating the causal
mechanisms that drive the behaviour of artificial agents. Six use cases are
covered, each addressing a typical question an analyst might ask about an
agent. In particular, we show that each question cannot be addressed by pure
observation alone, but instead requires conducting experiments with
systematically chosen manipulations so as to generate the correct causal
evidence.
