When transposing these modeling approaches to the cause-effect problem, we can see that some methods favor models with low complexity and low explanatory power such as linear Gaussian models while others have a better explanatory power as they can model complex interactions between noise and causes (for example when using generative neural networks), but at the cost of more complex mechanisms.

In the cause-effect pair literature, we can distinguish three different approaches used to deal with this complexity/fit trade-off:

• Method 1: Choose best fit.
• Method 2: Choose best Complexity
• Method 3: Weighted bi-crateria aggregation between fit and complexity