Some methods suffer from computational complexity limitations. In the bivariate case, there are only two alternative DAGs to compare, but for the multivariate case with more than 1000 variables, the number of different DAGs to consider grows exponentially.

Therefore it is a real challenge to make the successful methods of the cause-effect problem scale for big data problem. In particular the methods that can model complex interactions between cause and noise, such as those using Gaussian process regressions or neural networks are often really slow to compute and do not scale well in term of number of variables and number of data points.