Causal Intervention Framework for Variational Auto Encoder Mechanistic Interpretability (2505.03530v1)

Abstract: Mechanistic interpretability of deep learning models has emerged as a crucial research direction for understanding the functioning of neural networks. While significant progress has been made in interpreting discriminative models like transformers, understanding generative models such as Variational Autoencoders (VAEs) remains challenging. This paper introduces a comprehensive causal intervention framework for mechanistic interpretability of VAEs. We develop techniques to identify and analyze "circuit motifs" in VAEs, examining how semantic factors are encoded, processed, and disentangled through the network layers. Our approach uses targeted interventions at different levels: input manipulations, latent space perturbations, activation patching, and causal mediation analysis. We apply our framework to both synthetic datasets with known causal relationships and standard disentanglement benchmarks. Results show that our interventions can successfully isolate functional circuits, map computational graphs to causal graphs of semantic factors, and distinguish between polysemantic and monosemantic units. Furthermore, we introduce metrics for causal effect strength, intervention specificity, and circuit modularity that quantify the interpretability of VAE components. Experimental results demonstrate clear differences between VAE variants, with FactorVAE achieving higher disentanglement scores (0.084) and effect strengths (mean 4.59) compared to standard VAE (0.064, 3.99) and Beta-VAE (0.051, 3.43). Our framework advances the mechanistic understanding of generative models and provides tools for more transparent and controllable VAE architectures.

Causal Intervention Framework for Variational Autoencoder Mechanistic Interpretability

The paper "Causal Intervention Framework for Variational Autoencoder Mechanistic Interpretability" addresses a pivotal concern in the field of machine learning: the interpretability of generative models, specifically Variational Autoencoders (VAEs). While significant advancements have been made in understanding discriminative models like transformers, VAEs pose unique challenges due to their generative nature. This work introduces a robust causal intervention framework aimed at elucidating the internal mechanisms of VAEs by analyzing their "circuit motifs" and how these encode and process semantic factors.

Framework Overview

The framework devised in this paper is a multi-level causal intervention approach, integrating various techniques such as input manipulations, latent space perturbations, activation patching, and causal mediation analysis. These interventions are designed to dissect and comprehend the VAE's internal computational processes. By leveraging these methods, the authors successfully map computational graphs to causal graphs of semantic factors, thus enhancing the interpretability of VAEs.

Key Metrics and Findings

Central to this framework are several novel metrics introduced to quantify aspects of VAE interpretability:

• Causal Effect Strength: Measures the impact of interventions on the output, highlighting how strongly latent dimensions influence reconstructions.
• Intervention Specificity: Assesses the localization of an intervention's effect. High specificity indicates that interventions affect distinct output aspects.
• Circuit Modularity: Indicates the degree of specialization in the network's computational pathways, with higher values pointing to distinct and separate circuits for different semantic factors.

Experimental results demonstrate distinct differences between VAE variants: Standard VAE, $\beta$-VAE, and FactorVAE. Notably, FactorVAE achieved higher disentanglement scores and causal effect strengths than its counterparts, suggesting superior interpretability and consistent factor separation in its latent space.

Implications and Theoretical Contributions

This paper contributes a mechanistic perspective to the understanding of generative models by providing a detailed causal analysis of VAE components. The framework's ability to identify polysemantic versus monosemantic units within VAEs offers insights into the disentanglement process, a core challenge in developing effective latent variable models. Additionally, the "modularity paradox" uncovered by the authors—where high circuit modularity does not necessarily equate to high disentanglement—suggests that future model design should balance the separation of circuits with strong causal effects.

Future Directions

The framework's adaptability and robust analysis techniques pave the way for broader application across other generative models, such as GANs and diffusion models. Further exploration into combining insights from mechanistic interpretability with architectural innovations could lead to the development of more transparent, reliable, and controllable generative networks. Such advancements could significantly impact areas that demand high interpretability and control, such as medical imaging and autonomous systems.

In conclusion, this paper presents a comprehensive and structured approach to VAE interpretability through causal interventions, providing valuable tools and insights for the research community. The methodologies and findings hold potential to influence future developments in AI model transparency and accountability.