Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects but with the same underlying causal dynamics. We adapt continuous optimization for structure learning techniques to explicitly learn the cause and effects of the actions in an interactive environment and transfer to the target domain by categorization of the objects based on causal knowledge. We demonstrate the advantages of our approach in a gridworld setting by combining causal model-based approach with model-free approach in reinforcement learning.
Code (1)
Tasks
reinforcement-learningReinforcement Learning (RL)Transfer LearningSimilar Papers 제목 키워드 기반
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping
A major problem of causal inference is the arrangement of dependent nodes in a directed acyclic graph (DAG) with path coefficients and observed confounders. Path coefficients do not provide the units to measure the stren…
Causal InferenceExploring Transferability of Self-Supervised Learning by Task Conflict Calibration
In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively model this transferability? Transferability…
Self-Supervised LearningShadow-Mapping for Unsupervised Neural Causal Discovery
An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can ofte…
Causal DiscoveryLatent Convergent Cross Mapping
Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, …
Causal InferenceTime SeriesTime Series AnalysisMXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems
Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal interactions. Partial Cross Mapping (PCM) was …
Causal Discovery