Cover Image

Entropic Causal Inference: Graph Identifiability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Compton, Spencer, Greenewald, Kristjan, Katz, Dmitriy, Kocaoglu, Murat
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
by: Rahman, Md Musfiqur, et al.
Published: (2024)

Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
by: Ma, Rachel, et al.
Published: (2026)

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
by: Rahman, Md Musfiqur, et al.
Published: (2024)

Decomposing Epistemic Uncertainty for Causal Decision Making
by: Rahman, Md Musfiqur, et al.
Published: (2026)

Differentiable Constraint-Based Causal Discovery
by: Zhou, Jincheng, et al.
Published: (2025)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)

Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
by: Li, Allison, et al.
Published: (2025)

Neural Estimation for Scaling Entropic Multimarginal Optimal Transport
by: Tsur, Dor, et al.
Published: (2025)

Causal Discovery in Semi-Stationary Time Series
by: Gao, Shanyun, et al.
Published: (2024)

Causal Discovery-Driven Change Point Detection in Time Series
by: Gao, Shanyun, et al.
Published: (2024)

Interventional Causal Discovery in a Mixture of DAGs
by: Varıcı, Burak, et al.
Published: (2024)

Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
by: Gu, Anming, et al.
Published: (2024)

Activated LoRA: Fine-tuned LLMs for Intrinsics
by: Greenewald, Kristjan, et al.
Published: (2025)

Characterization and Learning of Causal Graphs from Hard Interventions
by: Zhou, Zihan, et al.
Published: (2025)

Sample Efficient Bayesian Learning of Causal Graphs from Interventions
by: Zhou, Zihan, et al.
Published: (2024)

CausalGuard: Conformal Inference under Graph Uncertainty
by: Singh, Vikash, et al.
Published: (2026)

A Closer Look at the Application of Causal Inference in Graph Representation Learning
by: Gao, Hang, et al.
Published: (2026)

Domain Adaptable Prescriptive AI Agent for Enterprise
by: Orderique, Piero, et al.
Published: (2024)

Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction
by: Hoang, Van Thuy, et al.
Published: (2026)

Individual Causal Inference with Structural Causal Model
by: Chang, Daniel T.
Published: (2025)

Local Causal Discovery for Statistically Efficient Causal Inference
by: Schubert, Mátyás, et al.
Published: (2025)

On the Granularity of Causal Effect Identifiability
by: Chen, Yizuo, et al.
Published: (2025)

Exploratory Causal Inference in SAEnce
by: Mencattini, Tommaso, et al.
Published: (2025)

Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
by: Brüel-Gabrielsson, Rickard, et al.
Published: (2024)

Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025)

Identifying General Mechanism Shifts in Linear Causal Representations
by: Chen, Tianyu, et al.
Published: (2024)

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)

Identifying Conditional Causal Effects in MPDAGs
by: LaPlante, Sara, et al.
Published: (2025)

On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
by: Fang, Zhuangyan, et al.
Published: (2022)

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
by: Komanduri, Aneesh, et al.
Published: (2023)

Identifying Causal Effects via Context-specific Independence Relations
by: Tikka, Santtu, et al.
Published: (2020)

Disentangled Representation Learning for Causal Inference with Instruments
by: Cheng, Debo, et al.
Published: (2024)

Deep Causal Learning: Representation, Discovery and Inference
by: Deng, Zizhen, et al.
Published: (2022)

Entropic Time Schedulers for Generative Diffusion Models
by: Stancevic, Dejan, et al.
Published: (2025)

Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
by: Elahi, Muhammad Qasim, et al.
Published: (2024)

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
by: Kim, Minsu, et al.
Published: (2025)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)

Learning Structural Causal Models from Ordering: Identifiable Flow Models
by: Le, Minh Khoa, et al.
Published: (2024)

Generative Conditional Distributions by Neural (Entropic) Optimal Transport
by: Nguyen, Bao, et al.
Published: (2024)

Scalable Simulation-free Entropic Unbalanced Optimal Transport
by: Choi, Jaemoo, et al.
Published: (2024)