OmniNet: an expandable causal inference network for multiple modalities

Yinuo Zhou, Georgios Leontidis, Mingjun Zhong

Abstract: When making a causal prediction, it is important to provide a detailed description of the pre-treatment situation to minimise the risk of overlooking a hidden yet significant variable. This often requires utilising various data formats to capture all relevant details. However, only a few causal models are able to handle complex data formats. An adaptable and expandable causal model capable of processing multiple data formats could address this limitation by incorporating new data formats and their processing units into the existing model and adjusting accordingly. This would improve the model’s estimation and enhance the accuracy of causal inference results. In this paper, we introduce and test an adaptable causal architecture to demonstrate its efficacy in handling both simple and complex data. The new model, named OmniNet, features a modular design that enables easy expansion and integration of different covariate types with minimal adjustments. Like an omnivorous animal feeding on both meat and plants, OmniNet can take different types of parameters once set. Our goal is to show that OmniNet performs on par with existing models when dealing with simple data while significantly improving accuracy when additional details are included. In the experiments, OmniNet had proved itself to be expandable and able to improve its performance by taking new covariates into concern. Such ability may allow it to work on more causal problems in a wider field.