Can causality accelerate experimentation in software systems?

Software designed using dataflow architecture naturally produces a graphical model of the data transformation process through the system. Interpreting this as a causal graph, we can leverage techniques from causal inference to estimate downstream effects of changes in code components, which can be interpreted as interventions within the causal graph. This allows for less costly software experimentation and can add another layer of protection against undesirable production updates.