Efficient Symbolic Computations for Identifying Causal Effects
Fuente:
arXiv
Saved in:
| Main Authors: | Hollering, Benjamin, Misra, Pratik, Sturma, Nils |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
by: Sturma, Nils, et al.
Published: (2025)
by: Sturma, Nils, et al.
Published: (2025)
Structural Identifiability of Graphical Continuous Lyapunov Models
by: Améndola, Carlos, et al.
Published: (2025)
by: Améndola, Carlos, et al.
Published: (2025)
Coarsening Causal DAG Models
by: Madaleno, Francisco, et al.
Published: (2026)
by: Madaleno, Francisco, et al.
Published: (2026)
Faithlessness in Gaussian graphical models
by: Drton, Mathias, et al.
Published: (2024)
by: Drton, Mathias, et al.
Published: (2024)
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
by: Hochsprung, Tom, et al.
Published: (2026)
by: Hochsprung, Tom, et al.
Published: (2026)
On the Granularity of Causal Effect Identifiability
by: Chen, Yizuo, et al.
Published: (2025)
by: Chen, Yizuo, et al.
Published: (2025)
Identifying Conditional Causal Effects in MPDAGs
by: LaPlante, Sara, et al.
Published: (2025)
by: LaPlante, Sara, et al.
Published: (2025)
A PC Algorithm for Max-Linear Bayesian Networks
by: Améndola, Carlos, et al.
Published: (2025)
by: Améndola, Carlos, et al.
Published: (2025)
On the Identifiability of Causal Abstractions
by: Li, Xiusi, et al.
Published: (2025)
by: Li, Xiusi, et al.
Published: (2025)
Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling
by: Sun, Xudong, et al.
Published: (2025)
by: Sun, Xudong, et al.
Published: (2025)
Testing Full Mediation of Treatment Effects and the Identifiability of Causal Mechanisms
by: Huber, Martin, et al.
Published: (2026)
by: Huber, Martin, et al.
Published: (2026)
Identifiability of Sparse Causal Effects using Instrumental Variables
by: Pfister, Niklas, et al.
Published: (2022)
by: Pfister, Niklas, et al.
Published: (2022)
Adaptive Problem Generation via Symbolic Representations
by: Yeo, Teresa, et al.
Published: (2026)
by: Yeo, Teresa, et al.
Published: (2026)
Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems
by: van Seeventer, Gijs, et al.
Published: (2026)
by: van Seeventer, Gijs, et al.
Published: (2026)
Identifying Causal Effects via Context-specific Independence Relations
by: Tikka, Santtu, et al.
Published: (2020)
by: Tikka, Santtu, et al.
Published: (2020)
On the Identifiability of Causal Graphs with the Invariance Principle
by: Montagna, Francesco
Published: (2025)
by: Montagna, Francesco
Published: (2025)
Identifying Causal Effects Under Functional Dependencies
by: Chen, Yizuo, et al.
Published: (2024)
by: Chen, Yizuo, et al.
Published: (2024)
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
by: Cai, Ruichu, et al.
Published: (2025)
by: Cai, Ruichu, et al.
Published: (2025)
Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)
by: Scholl, Philipp, et al.
Published: (2022)
Neuro-Symbolic Rule Lists
by: Xu, Sascha, et al.
Published: (2024)
by: Xu, Sascha, et al.
Published: (2024)
Identifying Causal Effects Using a Single Proxy Variable
by: Vollmer, Silvan, et al.
Published: (2026)
by: Vollmer, Silvan, et al.
Published: (2026)
CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
by: Jawahar, Pratik, et al.
Published: (2026)
by: Jawahar, Pratik, et al.
Published: (2026)
A Multi-Level Causal Intervention Framework for Mechanistic Interpretability in Variational Autoencoders
by: Roy, Dip, et al.
Published: (2025)
by: Roy, Dip, et al.
Published: (2025)
General Identifiability and Achievability for Causal Representation Learning
by: Varıcı, Burak, et al.
Published: (2023)
by: Varıcı, Burak, et al.
Published: (2023)
Identifying Weight-Variant Latent Causal Models
by: Liu, Yuhang, et al.
Published: (2022)
by: Liu, Yuhang, et al.
Published: (2022)
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
by: Dwivedi, Chaitanya, et al.
Published: (2026)
by: Dwivedi, Chaitanya, et al.
Published: (2026)
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
by: Thams, Nikolaj, et al.
Published: (2022)
by: Thams, Nikolaj, et al.
Published: (2022)
Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
by: Boileau, Philippe, et al.
Published: (2026)
by: Boileau, Philippe, et al.
Published: (2026)
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
by: Cheng, Debo, et al.
Published: (2024)
by: Cheng, Debo, et al.
Published: (2024)
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
by: Holzschuh, Benjamin, et al.
Published: (2025)
by: Holzschuh, Benjamin, et al.
Published: (2025)
Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data
by: Roy, Dip, et al.
Published: (2026)
by: Roy, Dip, et al.
Published: (2026)
Identifying Causal Direction via Variational Bayesian Compression
by: Tran, Quang-Duy, et al.
Published: (2025)
by: Tran, Quang-Duy, et al.
Published: (2025)
On Theoretical Identifiability of Discrete Latent Causal Graphical Models
by: Lee, Seunghyun, et al.
Published: (2025)
by: Lee, Seunghyun, et al.
Published: (2025)
Entropic Causal Inference: Graph Identifiability
by: Compton, Spencer, et al.
Published: (2025)
by: Compton, Spencer, et al.
Published: (2025)
Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models
by: Biswas, Preetom, et al.
Published: (2026)
by: Biswas, Preetom, et al.
Published: (2026)
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
by: Thielmann, Anton, et al.
Published: (2025)
by: Thielmann, Anton, et al.
Published: (2025)
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
by: Wen, Hechuan, et al.
Published: (2024)
by: Wen, Hechuan, et al.
Published: (2024)
Identifiable Latent Polynomial Causal Models Through the Lens of Change
by: Liu, Yuhang, et al.
Published: (2023)
by: Liu, Yuhang, et al.
Published: (2023)
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
by: Welch, Ryan, et al.
Published: (2024)
by: Welch, Ryan, et al.
Published: (2024)
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
by: Morioka, Hiroshi, et al.
Published: (2023)
by: Morioka, Hiroshi, et al.
Published: (2023)
Similar Items
-
Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
by: Sturma, Nils, et al.
Published: (2025) -
Structural Identifiability of Graphical Continuous Lyapunov Models
by: Améndola, Carlos, et al.
Published: (2025) -
Coarsening Causal DAG Models
by: Madaleno, Francisco, et al.
Published: (2026) -
Faithlessness in Gaussian graphical models
by: Drton, Mathias, et al.
Published: (2024) -
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
by: Hochsprung, Tom, et al.
Published: (2026)