Causal structure learning
The last source of causal knowledge that we will discuss in this chapter is causal structure learning. Causal structure learning (sometimes used interchangeably with causal discovery) is a set of methods aiming at recovering the structure of the data-generating process from the data generated by this process. Traditional causal discovery focused on recovering the causal structure from observational data only.
Some more recent methods allow for encoding expert knowledge into the graph or learning from interventional data (with known or unknown interventions).
Causal structure learning might be much cheaper and faster than running an experiment, but it often turns out to be challenging in practice.
Many causal structure learning methods require no hidden confounding – a condition difficult to guarantee in numerous real-world scenarios. Some causal discovery methods try to overcome this limitation with some success.
