Cover Image

Differentiable Cyclic Causal Discovery Under Unmeasured Confounders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sethuraman, Muralikrishnna G., Fekri, Faramarz
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
by: Sethuraman, Muralikrishnna G., et al.
Published: (2026)

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

Learning Cyclic Causal Models from Incomplete Data
by: Sethuraman, Muralikrishnna G., et al.
Published: (2024)

MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
by: Sethuraman, Muralikrishnna G., et al.
Published: (2024)

Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
by: Boileau, Philippe, et al.
Published: (2026)

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
by: Li, Yuhan, et al.
Published: (2025)

Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)

Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
by: Lorbeer, Boris, et al.
Published: (2024)

Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding
by: Peña, Jose M.
Published: (2024)

Sequential Treatment Effect Estimation with Unmeasured Confounders
by: Wang, Yingrong, et al.
Published: (2025)

Automating the Selection of Proxy Variables of Unmeasured Confounders
by: Xie, Feng, et al.
Published: (2024)

Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
by: Wu, Yong, et al.
Published: (2025)

Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
by: Morinishi, Yoshimitsu, et al.
Published: (2025)

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
by: Schkoda, Daniela, et al.
Published: (2024)

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
by: Peña, Jose M.
Published: (2024)

Stable Differentiable Causal Discovery
by: Nazaret, Achille, et al.
Published: (2023)

Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
by: Waxman, Daniel, et al.
Published: (2024)

Causal Effect Identification in LiNGAM Models with Latent Confounders
by: Tramontano, Daniele, et al.
Published: (2024)

Causal Inference with Categorical Unobserved Confounder via Mixture Learning
by: Saha, Aytijhya, et al.
Published: (2026)

Identification of Average Causal Effects in Confounded Additive Noise Models
by: Elahi, Muhammad Qasim, et al.
Published: (2024)

Causal Discovery in Structural VAR Models Under Equal Noise Variance
by: HasanAbadi, SeyedSina Seyedi, et al.
Published: (2026)

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
by: Hafer, Marlies, et al.
Published: (2025)

The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2025)

Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
by: Zou, Yushu, et al.
Published: (2025)

When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
by: Liu, Muye, et al.
Published: (2025)

SC3D: Dynamic and Differentiable Causal Discovery for Temporal and Instantaneous Graphs
by: Das, Sourajit, et al.
Published: (2026)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
by: Lee, Po-Han, et al.
Published: (2025)

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
by: Oprescu, Miruna, et al.
Published: (2025)

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
by: Brouillard, Philippe, et al.
Published: (2024)

Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023)

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
by: Liang, Wei, et al.
Published: (2025)

Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
by: Luo, Gongxu, et al.
Published: (2025)

Differentiable Structure Learning and Causal Discovery for General Binary Data
by: Deng, Chang, et al.
Published: (2025)

Predictive Performance Comparison of Decision Policies Under Confounding
by: Guerdan, Luke, et al.
Published: (2024)

A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)

CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
by: Kong, Lingbai, et al.
Published: (2024)

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024)