Cover Image

MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ong, Hans Jarett, Chikahara, Yoichi, Iwata, Tomoharu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
by: Chikahara, Yoichi
Published: (2026)

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables
by: Chikahara, Yoichi
Published: (2026)

Meta-learning for Positive-unlabeled Classification
by: Kumagai, Atsutoshi, et al.
Published: (2024)

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
by: Chikahara, Yoichi, et al.
Published: (2024)

Scalable Contrastive Causal Discovery under Unknown Soft Interventions
by: Zhang, Mingxuan, et al.
Published: (2026)

Meta-learning Representations for Learning from Multiple Annotators
by: Kumagai, Atsutoshi, et al.
Published: (2025)

Meta-Learning for Neural Network-based Temporal Point Processes
by: Takimoto, Yoshiaki, et al.
Published: (2024)

Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
by: Ong, Hans Jarett J., et al.
Published: (2025)

A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization
by: Kim, Hideaki, et al.
Published: (2025)

Regret-Based Federated Causal Discovery with Unknown Interventions
by: Baldo, Federico, et al.
Published: (2025)

A Meta-Learning Approach to Bayesian Causal Discovery
by: Dhir, Anish, et al.
Published: (2024)

Learning Mixtures of Unknown Causal Interventions
by: Kumar, Abhinav, et al.
Published: (2024)

SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
by: Turkoglu, Alpar, et al.
Published: (2026)

Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
by: Iwata, Tomoharu, et al.
Published: (2025)

Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
by: Yoshikawa, Yuya, et al.
Published: (2023)

Unified generalization analysis for physics informed neural networks
by: Hashimoto, Yuka, et al.
Published: (2026)

Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices
by: Hashimoto, Yuka, et al.
Published: (2024)

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
by: Dhir, Anish, et al.
Published: (2025)

K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
by: Kim, Hideaki, et al.
Published: (2025)

Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
by: Tanaka, Yusuke, et al.
Published: (2024)

Linear Causal Representation Learning from Unknown Multi-node Interventions
by: Varıcı, Burak, et al.
Published: (2024)

Linear Causal Bandits: Unknown Graph and Soft Interventions
by: Yan, Zirui, et al.
Published: (2024)

Bayesian Intervention Optimization for Causal Discovery
by: Wang, Yuxuan, et al.
Published: (2024)

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)

MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning
by: Wu, Jin, et al.
Published: (2025)

Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
by: Ong, Hans Jarett J., et al.
Published: (2024)

CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representation
by: Zhang, Zhengzhe, et al.
Published: (2025)

Linear Causal Discovery with Interventional Constraints
by: Guo, Zhigao, et al.
Published: (2025)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting
by: Liu, Anxian, et al.
Published: (2025)

SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
by: Papenmeier, Leonard, et al.
Published: (2026)

Curious Causality-Seeking Agents Learn Meta Causal World
by: Zhao, Zhiyu, et al.
Published: (2025)

Large-Scale Bayesian Causal Discovery with Interventional Data
by: Han, Seong Woo, et al.
Published: (2025)

Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
by: Yang, Yuqin, et al.
Published: (2023)

Federated Causal Discovery From Interventions
by: Abyaneh, Amin, et al.
Published: (2022)

TriOpt: A Scalable Algorithm for Linear Causal Discovery
by: Joy, Rafat Ashraf, et al.
Published: (2026)

Scalable Meta-Learning via Mixed-Mode Differentiation
by: Kemaev, Iurii, et al.
Published: (2025)

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
by: Amin, Alan Nawzad, et al.
Published: (2024)

Causal Discovery by Interventions via Integer Programming
by: Elrefaey, Abdelmonem, et al.
Published: (2024)

Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
by: Panayiotou, Panayiotis, et al.
Published: (2026)