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.
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal relationship/structure among variables. In recent years, an Independent Mechanism (IM) principle was proposed, stating that the mechanism generating the cause and the one mapping the cause to the effect are independent. As the conjecture, it is argued that in the causal direction, the conditional distributions instantiated at different value of the conditioning variable have less variation than the anti-causal directio…
Coupled dynamical systems are frequently observed in nature, but often not well understood in terms of their causal structure without additional domain knowledge about the system. Especially when analyzing observational time series data of dynamical systems where it is not possible to conduct controlled experiments, for example time series of climate variables, it can be challenging to determine how features causally influence each other. There are many techniques available to recover causal relationships from data, such as Granger causality, convergent cross mapping, and causal graph structur…
Technion（以色列理工学院）；Google Research（谷歌研究院）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into noise sources and deterministic functions mapping realizations of noise to samples. Unfortunately, the causal mechanism is not uniquely identified by data that can be gathered by observing and interacting with the world, so there remains the question of how to choose causal mechanisms. In recent work, Oberst & Sontag (2019) propose Gumbel-max SCMs, which use Gumbel-max reparameterizations as the causal mechanism due to a…
Responsible AI Research Centre（负责任人工智能研究中心）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；School of Computer Science and Engineering（计算机科学与工程学院）；School of Mathematics and Statistics（数学与统计学学院）；Halicioğlu Data Science Institute（Halicioğlu数据科学研究所）；Department of Philosophy（哲学系）
Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地球科学学院经济地质局）
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially when parameter dimensionality is high. Deep learning has opened a new door toward knowledge representation and complex pattern identification. In particular, the recently introduced generative adversarial networks (GANs) hold strong promises in learning cross-domain mappings for …
Oden Institute for Computational Engineering and Sciences, University of Texas, Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学研究所）
Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks. We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract met…
CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
LIVIA, École de Technologie Supérieure (ÉTS), Université du Québec（LIVIA，École de Technologie Supérieure（ÉTS），Université du Québec）；Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR)（研究生信息学项目（PPGIa），Pontifícia Universidade Católica do Paraná（PUCPR））
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause $P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as independent random variables, we propose to infer the causal direction via comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the distance correlation between $P(Y)$ and $P(X|Y)$. We infer "$X$ causes $Y$" if the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments are performed to show the performance of the proposed method.
DeepMind（深度思维）；Mila（米拉（魁北克人工智能研究所））；Polytechnique Montreal（蒙特利尔理工学院）；University of Montreal（蒙特利尔大学）；Google Research（谷歌研究院）
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms operate by generating candidate graphs and evaluating them using either score-based methods (including continuous optimization) or independence tests. In our work, we instead treat the inference process as a black box and design a neural network architecture that learns the mapping from both observational and interventional data to graph structures via supervised training on synthetic graphs. The learned model general…
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
University of Amsterdam（阿姆斯特丹大学）；Institute for Logic, Language and Computation (ILLC)（逻辑、语言与计算研究所）；University of Edinburgh（爱丁堡大学）；Institute for Language, Cognition and Computation (ILCC)（语言、认知与计算研究所）
Exhaustive Circuit Mapping of a Single-Cell Foundation Model Reveals Massive Redundancy, Heavy-Tailed Hub Architecture, and Layer-Dependent Differentiation Control
Department of Computer Science, University of Tübingen（图宾根大学计算机科学系）
