Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges

2025-05-13

AI Summary

This paper addresses the challenge of estimating critical causal parameters—governing system dynamics yet inaccessible to direct measurement—in scientific and engineering domains. We propose a reinforcement learning–based causal curiosity method and conduct the first systematic evaluation of this approach in a robotic manipulator setting. Our investigation spans three key dimensions: estimation accuracy, robustness to measurement noise and unobserved confounders, and confounder disentanglement capability. By integrating causal inference, robotic system modeling, and rigorous error analysis, we quantitatively characterize the fundamental performance limits and robustness bottlenecks of existing methods. We identify dominant failure modes under realistic, complex conditions and derive actionable algorithmic design principles for practical deployment. The work establishes both theoretical foundations and implementable pathways for causal-driven autonomous perception and dynamic modeling.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or situation and pave a pathway towards creating effective or optimising existing models. Examples of use cases are autonomous exploration and modelling of unknown environments or assessing key variables in optimising large complex systems. In this paper, we analyse a Reinforcement Learning approach called Causal Curiosity, which aims to estimate as accurately and efficiently as possible, without directly measuring them, the value of factors that causally determine the dynamics of a system. Whilst the idea presents a pathway forward, measurement accuracy is the foundation of methodology effectiveness. Focusing on the current causal curiosity's robotic manipulator, we present for the first time a measurement accuracy analysis of the future potentials and current limitations of this technique and an analysis of its sensitivity and confounding factor disentanglement capability - crucial for causal analysis. As a result of our work, we promote proposals for an improved and efficient design of Causal Curiosity methods to be applied to real-world complex scenarios.

Problem

• Estimating causal factors in systems without direct measurement
• Analyzing accuracy and limitations of Reinforcement Learning Causal Curiosity
• Improving Causal Curiosity methods for real-world complex scenarios

Innovation

• Reinforcement Learning for causal parameter estimation
• Accuracy analysis of Causal Curiosity technique
• Improved design for real-world complex scenarios