Variational GAN-Enhanced Causal Effect Inference for Interpretable Learning in Heterogeneous IoT Systems

While data-driven learning techniques are revolutionizing the smart application design and development, fundamental issues of lacking generalizability and interpretability are posing great threats to their effectiveness in real-world implementations. Such weaknesses can be complemented by advanced Artificial Intelligence (AI) models that look at deeper relationships of association, intervention, and counterfactual, toward interpretable learning especially when facing heterogeneous big data generated through distributed devices or sensors in modern Internet of Things (IoT) systems. In this paper, we design and introduce a so-called Variational GAN with Causal Heterogeneous Graph (VGAN-CHG) model to facilitate the causality-inspired interpretable learning, which can effectively enhance the causal effect inference with the help of reconstructed latent variables in terms of the discovered temporal causality and enriched variational representation, and further generate more realistic counterfactuals for optimized Individual Treatment Effect (ITE) estimation, when tackling the issue of limited observational data in heterogeneous IoT systems. In particular, the variational GAN, incorporating a Bipartite Variational Autoencoder (BVAE) into a conditional GAN structure, is newly designed and applied to realize the Variational Representation Learning (VRL) and adversarial training together. While the CHG, constructed with a Graph Attention Network (GAT) mechanism, is first defined and implemented to seamlessly capture and model the covariational feature correlation, temporal dependency, and structural causal relationship in a unified form, so as to enhance the causal representation learning for feature-level interpretability and credibility in heterogeneous IoT scenarios. In addition, the BVAE, is devised and built to capture and reconstruct the so-called global and regional latent variables in terms of the sequential global context and time-specific fluctuation from inter-/intra-time intervals, thus can jointly optimize the bias reduction, counterfactual reasoning, and accuracy of ITE estimation. A refined representation learning scheme is then developed, in which the Dynamic Bayesian Network (DBN) is involved to enhance the cross-interval dependency considering the fused temporal causality effect, while the Similarity-Preserving Data Mapping (SPDM) is integrated to improve the geometry-aware feature consistency between the original and learned latent space, resulting in the further feature refinement with enriched interpretable context and sufficient variability. Experiment and evaluation results based on three public datasets demonstrate that the proposed model is able to achieve higher learning efficiency, better bias reduction, and optimal treatment estimation with refined feature representation, compared with six other state-of-the-art similar learning methods for modern intelligent system design and smart application deployment in heterogeneous environments.