Causal Structure Learning Using PCMCI+ and Path Constraints from Wavelet-Based Soft Interventions

The discovery of causal relations via interventions has proven to be simple when only one observed variable is affected or unaffected. However, in a multivariate setting, it is likely that more than one variable is affected by an intervention and thus drawing conclusions about the causal relations becomes more difficult as the gained information is ambiguous. To deal with this, we introduce a novel definition of path constraints and to obtain them, we came up with a novel approach for wavelet-based interventions. We demonstrate our approach on a combustion engine simulation, where we injected wavelets of our choice in an actuated variable and tried to rediscover them in the other, observed variables to gain path constraints. Subsequently, we demonstrate how to use these constraints to optimize the results of the established PCMCI+ algorithm.