  Understanding how images influence the world, interpreting which effects
their semantics have on various quantities and exploring the reasons behind
changes in image-based predictions are highly difficult yet extremely
interesting problems. By adopting a holistic modeling approach utilizing Neural
Additive Models in combination with Diffusion Autoencoders, we can effectively
identify the latent hidden semantics of image effects and achieve full
intelligibility of additional tabular effects. Our approach offers a high
degree of flexibility, empowering us to comprehensively explore the impact of
various image characteristics. We demonstrate that the proposed method can
precisely identify complex image effects in an ablation study. To further
showcase the practical applicability of our proposed model, we conduct a case
study in which we investigate how the distinctive features and attributes
captured within host images exert influence on the pricing of Airbnb rentals.
