Causal-Symbolic Meta-Learning (CSML): Inducing Causal World Models for Few-Shot Generalization (2509.12387v1)

Abstract: Modern deep learning models excel at pattern recognition but remain fundamentally limited by their reliance on spurious correlations, leading to poor generalization and a demand for massive datasets. We argue that a key ingredient for human-like intelligence-robust, sample-efficient learning-stems from an understanding of causal mechanisms. In this work, we introduce Causal-Symbolic Meta-Learning (CSML), a novel framework that learns to infer the latent causal structure of a task distribution. CSML comprises three key modules: a perception module that maps raw inputs to disentangled symbolic representations; a differentiable causal induction module that discovers the underlying causal graph governing these symbols and a graph-based reasoning module that leverages this graph to make predictions. By meta-learning a shared causal world model across a distribution of tasks, CSML can rapidly adapt to novel tasks, including those requiring reasoning about interventions and counterfactuals, from only a handful of examples. We introduce CausalWorld, a new physics-based benchmark designed to test these capabilities. Our experiments show that CSML dramatically outperforms state-of-the-art meta-learning and neuro-symbolic baselines, particularly on tasks demanding true causal inference.

• The paper introduces CSML, a framework that leverages causal reasoning to build causal world models for effective few-shot generalization.
• It integrates a perception module, a causal induction module, and a reasoning module to construct and utilize a causal DAG for task-specific predictions.
• Experimental results on the CausalWorld benchmark show that CSML outperforms existing approaches with superior sample efficiency and robustness.

Causal-Symbolic Meta-Learning (CSML): Inducing Causal World Models for Few-Shot Generalization

Introduction

The paper introduces Causal-Symbolic Meta-Learning (CSML), a framework seeking to enhance the adaptation capabilities of machine learning models in few-shot learning scenarios by leveraging causal inference. CSML aims to overcome the limitations of traditional deep learning models, which are often burdened by dependency on spurious correlations and require extensive data for effective generalization.

Motivation

Deep learning models often rely heavily on patterns and correlations in the data they are trained on, making them fragile when faced with out-of-distribution tasks that deviate from their training set. In contrast, human cognition exhibits robustness and sample efficiency by understanding causal mechanisms underlying observed phenomena. CSML addresses this by incorporating causal reasoning into the learning process, promoting better generalization with fewer examples.

CSML Framework

CSML comprises three primary modules, each integral to the framework's objective of learning and utilizing causal structures:

1. Perception Module ($\phi_{enc}$): This module converts raw, high-dimensional inputs (like images) into low-dimensional, disentangled symbolic representations using deep neural networks, effectively serving as an encoder.
2. Causal Induction Module ($\phi_{causal}$): This module is responsible for constructing a Directed Acyclic Graph (DAG) that represents the causal relationships between different symbolic variables derived from the perception module. It adopts techniques from differentiable causal discovery methodologies, ensuring the induce graph adheres to causal constraints.
3. Reasoning Module ($\phi_{reason}$): Utilizing Graph Neural Networks (GNNs), this module applies the causal graph for task-specific predictions, allowing for message-passing and inference typically required in causal reasoning.

The framework is designed to support meta-learning, where it learns shared causal structures across various tasks and applies these to new, unseen tasks, particularly those requiring reasoning about interventions and counterfactuals.

Theoretical Analysis

A key contribution of this work is the theoretical generalization bound that relates the learned causal graph's correctness to the model's few-shot task performance. This mathematical guarantee links the Structural Hamming Distance (SHD) of the discovered graph to the generalization error, suggesting that more accurate causal graphs correspond to better performance.

The CausalWorld Benchmark

The work presents CausalWorld, a novel benchmark developed to evaluate the causal reasoning capabilities of the CSML framework. CausalWorld challenges models with tasks demanding predictive, interventional, and counterfactual reasoning. These tasks are set up within a controlled physics simulation, requiring models to demonstrate genuine understanding, beyond mere correlation mapping found in traditional datasets.

Experimental Results

Experiments indicate that CSML significantly outperforms existing state-of-the-art meta-learning approaches, especially in scenarios requiring causal inference. In tasks involving prediction, interventions, and counterfactual reasoning, CSML displayed superior sample efficiency and robustness. The results substantiated the framework's ability to rapidly generalize to new tasks with minimal data.

Implementation Considerations

The implementation of CSML involves careful architectural and training considerations:

• Computational Load: Utilizing both graph-based reasoning and differentiable causal inference introduces additional computation that must be optimized for efficiency.
• Scalability: While effective in the tested domains, scaling this framework to higher-dimensional or more complex causal structures remains an area for further research.
• Hardware Requirements: Leveraging deep networks for the perception and reasoning modules may demand substantial computational resources, particularly when deploying in real-time applications.

Conclusion

Causal-Symbolic Meta-Learning represents a promising step towards integrating causal reasoning into learning frameworks, fostering the development of AI systems that can learn and adapt with a human-like understanding of the world. Future developments could focus on refining the causal discovery process and extending the framework's applicability to broader domains encompassing larger-scale and more diverse datasets. This research underscores the potential for causal modeling to enhance the generalization capabilities of AI, contributing to more robust and adaptable intelligent systems.