Kweku Opoku-Agyemang
Working Paper Class 46
In this paper, we introduce Large Causal Behavioral Models (LCBMs), an innovative extension of Large Behavioral Models (LBMs) that incorporate causal inference to enhance decision-making, interpretability, robustness, generalization, counterfactual reasoning, and bias mitigation. By leveraging causal relationships, LCBMs aim to provide more reliable and transparent AI systems capable of performing complex tasks in dynamic environments. We begin by exploring the theoretical underpinnings of LCBMs, focusing on regret bounds and proofs of impact. We present theorems that demonstrate how causal inference can improve decision-making by minimizing regret in sequential decision processes. Simulations demonstrate how LCBMs enhance decision-making by identifying causal relationships between actions and outcomes, leading to more effective and efficient task execution. Additionally, we show how LCBMs improve interpretability by providing clear explanations for their decisions, increase robustness and generalization by focusing on causal mechanisms, enable counterfactual reasoning for better planning, and mitigate biases to ensure fairer outcomes. We extend LCBMs to multi-modal (visual, tactile, auditory) data. We also extend LCBMs to incorporate human-in-the-loop learning to guide and correct the model; develop hierarchical causal models for long-horizon tasks with spare rewards to address some key challenges in reinforcement learning; and close with rigorous theoretical foundations including regret bounds, sample complexity characterizations and formal guarantees for causal transfer learning.
The views in this Working Paper Class are those of the authors, not necessarily of Machine Learning X Doing.
Opoku-Agyemang, Kweku A. (2025). "Large Causal Behavioral Models: Integrating Multi-Modal Data, Expertise and Hierarchical Learning for Robotics." Machine Learning X Doing Working Paper Class 46. Machine Learning X Doing.
Copyright © 2025 Machine Learning X Doing Incorporated. All Rights Reserved.
