Extra – going beyond observations
In certain cases, we might be able to intervene on some or all variables in order to facilitate or improve the results of a causal discovery process.
In this short section, we’ll introduce two methods that can help us make sure that we make good use of such interventions.
ENCO
Efficient Neural Causal Discovery (ENCO; Lippe et al., 2022) is a causal discovery method for observational and interventional data. It uses continuous optimization and – as we mentioned earlier in the section on DECI – parametrizes edge existence and its orientation separately. ENCO is guaranteed to converge to a correct DAG if interventions on all variables are available, but it also performs reasonably well on partial intervention sets. Moreover, the model works with discrete, continuous, and mixed variables and can be extended to work with hidden confounding. The model code is available on GitHub (https://bit.ly/EncoGitHub).
