:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xie, Yifei, Huang, Jian
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Methodology
Online Access:	https://arxiv.org/abs/2605.05890
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning
by: Li, Haoze, et al.
Published: (2025)

Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)

Causal Effect Estimation with Learned Instrument Representations
by: Dean, Frances, et al.
Published: (2026)

Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
by: Cabezas, Alberto, et al.
Published: (2024)

Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
by: Clivio, Oscar, et al.
Published: (2026)

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

Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)

Marginal Causal Flows for Validation and Inference
by: Manela, Daniel de Vassimon, et al.
Published: (2024)

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
by: Wu, Dongze, et al.
Published: (2025)

The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
by: Fernández-Loría, Carlos, et al.
Published: (2025)

Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
by: Nanavati, Praharsh, et al.
Published: (2025)

Flow-based Distributionally Robust Optimization
by: Xu, Chen, et al.
Published: (2023)

The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
by: Machlanski, Damian, et al.
Published: (2023)

Considerations for Estimating Causal Effects of Informatively Timed Treatments
by: Oganisian, Arman
Published: (2025)

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

DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
by: Zhu, Jiageng, et al.
Published: (2024)

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
by: Ter-Minassian, Lucile, et al.
Published: (2024)

Compositional Models for Estimating Causal Effects
by: Pruthi, Purva, et al.
Published: (2024)

Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
by: Hoang, Nu, et al.
Published: (2024)

Discrete Causal Representation Learning
by: Zhang, Wenjin, et al.
Published: (2026)

Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)

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

Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
by: Tanaka, Masahiro
Published: (2025)

A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
by: Pham, Khiem, et al.
Published: (2023)

Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
by: Wiederkehr, Christoph, et al.
Published: (2025)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
by: Xie, Feng, et al.
Published: (2023)

FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
by: Dong, Yuexiao, et al.
Published: (2026)

A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)

Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
by: Pros, Roger, et al.
Published: (2024)

The Estimation of Continual Causal Effect for Dataset Shifting Streams
by: Chen, Baining, et al.
Published: (2025)

NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
by: Reddy, Abbavaram Gowtham, et al.
Published: (2022)

Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
by: Cui, Ziqiang, et al.
Published: (2024)

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)

Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026)

Undersmoothing Causal Estimators with Generative Trees
by: Machlanski, Damian, et al.
Published: (2022)

A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation
by: Shi, Jieru, et al.
Published: (2023)

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
by: Mahajan, Divyat, et al.
Published: (2022)

Introducing Diminutive Causal Structure into Graph Representation Learning
by: Gao, Hang, et al.
Published: (2024)

Average Causal Effect Estimation in DAGs with Hidden Variables: Beyond Back-Door and Front-Door Criteria
by: Guo, Anna, et al.
Published: (2024)

C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference
by: Dalvi, Abhishek, et al.
Published: (2025)