A **Parameter Encoder Neural Network (PENN)** (Pfitzinger 2021) is an explainable machine learning technique that solves two problems associated with traditional XAI algorithms:

- It permits the calculation of local parameter distributions. Parameter distributions are often more interesting than feature contributions — particularly in economic and financial applications — since the parameters disentangle the effect from the observation (the contribution can roughly be defined as the demeaned product of effect and observation).
- It solves a problem of biased contributions that is inherent to many traditional XAI algorithms. Particularly in the setting where neural networks are powerful — in interactive, dependent processes — traditional XAI can be biased, by attributing effect to each feature independently.

At the end of the tutorial, I will have estimated the following highly nonlinear parameter functions for a simulated regression with three variables:

A Github version of the code can be found here.