CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time
Fuente:
arXiv
Saved in:
| Main Authors: | Nguyen, Nghia D., Robles-Granda, Pablo, Varshney, Lav R. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Meta-Learning Perspective on Transformers for Causal Language Modeling
by: Wu, Xinbo, et al.
Published: (2023)
by: Wu, Xinbo, et al.
Published: (2023)
Online Reinforcement Learning with Passive Memory
by: Pattanaik, Anay, et al.
Published: (2024)
by: Pattanaik, Anay, et al.
Published: (2024)
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
by: Hartman, Max, et al.
Published: (2025)
by: Hartman, Max, et al.
Published: (2025)
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
by: Garriga, Tomàs, et al.
Published: (2026)
by: Garriga, Tomàs, et al.
Published: (2026)
Federated Learning via Lattice Joint Source-Channel Coding
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
by: Kim, Benjamin D., et al.
Published: (2026)
by: Kim, Benjamin D., et al.
Published: (2026)
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
by: Bouchattaoui, Mouad El, et al.
Published: (2023)
by: Bouchattaoui, Mouad El, et al.
Published: (2023)
Efficient Model-Agnostic Multi-Group Equivariant Networks
by: Baltaji, Razan, et al.
Published: (2023)
by: Baltaji, Razan, et al.
Published: (2023)
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)
by: Zhou, Huozhi, et al.
Published: (2020)
Federated Nonlinear System Identification
by: Tupe, Omkar, et al.
Published: (2025)
by: Tupe, Omkar, et al.
Published: (2025)
Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models
by: Hartman, Max, et al.
Published: (2025)
by: Hartman, Max, et al.
Published: (2025)
Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
by: Jiang, Junqi, et al.
Published: (2025)
by: Jiang, Junqi, et al.
Published: (2025)
Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
by: Wang, Zilong, et al.
Published: (2025)
by: Wang, Zilong, et al.
Published: (2025)
Beyond Pooling: Matching for Robust Generalization under Data Heterogeneity
by: Roy, Ayush, et al.
Published: (2026)
by: Roy, Ayush, et al.
Published: (2026)
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
by: Komanduri, Aneesh, et al.
Published: (2024)
by: Komanduri, Aneesh, et al.
Published: (2024)
Considerations for Estimating Causal Effects of Informatively Timed Treatments
by: Oganisian, Arman
Published: (2025)
by: Oganisian, Arman
Published: (2025)
A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search
by: Ellis-Mohr, Austin R., et al.
Published: (2025)
by: Ellis-Mohr, Austin R., et al.
Published: (2025)
Causal Contrastive Learning for Counterfactual Regression Over Time
by: Bouchattaoui, Mouad El, et al.
Published: (2024)
by: Bouchattaoui, Mouad El, et al.
Published: (2024)
Counterfactual Generative Models for Time-Varying Treatments
by: Wu, Shenghao, et al.
Published: (2023)
by: Wu, Shenghao, et al.
Published: (2023)
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
by: Dang, Hien, et al.
Published: (2023)
by: Dang, Hien, et al.
Published: (2023)
Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
by: Nguyen, Nang Hung, et al.
Published: (2026)
by: Nguyen, Nang Hung, et al.
Published: (2026)
Disentangled Graph Autoencoder for Treatment Effect Estimation
by: Fan, Di, et al.
Published: (2024)
by: Fan, Di, et al.
Published: (2024)
Conditional Counterfactual Mean Embeddings: Doubly Robust Estimation and Learning Rates
by: Anancharoenkij, Thatchanon, et al.
Published: (2026)
by: Anancharoenkij, Thatchanon, et al.
Published: (2026)
Flow Autoencoders are Effective Protein Tokenizers
by: Dilip, Rohit, et al.
Published: (2025)
by: Dilip, Rohit, et al.
Published: (2025)
Estimating Aleatoric Uncertainty in the Causal Treatment Effect
by: Xu, Liyuan, et al.
Published: (2026)
by: Xu, Liyuan, et al.
Published: (2026)
Retrospective Feature Estimation for Continual Learning
by: Nguyen, Nghia D., et al.
Published: (2024)
by: Nguyen, Nghia D., et al.
Published: (2024)
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
by: Huang, Qiang, et al.
Published: (2024)
by: Huang, Qiang, et al.
Published: (2024)
DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
by: Zhu, Jiageng, et al.
Published: (2024)
by: Zhu, Jiageng, et al.
Published: (2024)
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
by: Wen, Hechuan, et al.
Published: (2025)
by: Wen, Hechuan, et al.
Published: (2025)
Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models
by: Bender, Sidney, et al.
Published: (2026)
by: Bender, Sidney, et al.
Published: (2026)
Transformer-based Causal Language Models Perform Clustering
by: Wu, Xinbo, et al.
Published: (2024)
by: Wu, Xinbo, et al.
Published: (2024)
Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)
by: Bynum, Lucius E. J., et al.
Published: (2025)
Sparse classification with positive-confidence data in high dimensions
by: Mai, The Tien, et al.
Published: (2025)
by: Mai, The Tien, et al.
Published: (2025)
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
by: Varshney, Payal, et al.
Published: (2024)
by: Varshney, Payal, et al.
Published: (2024)
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
by: Singhal, Raghav, et al.
Published: (2025)
by: Singhal, Raghav, et al.
Published: (2025)
From Baseline to Follow-Up: Counterfactual Spine DXA Image Synthesis in UK Biobank Using a Causal Hierarchical Variational Autoencoder
by: Zhang, Yilin, et al.
Published: (2026)
by: Zhang, Yilin, et al.
Published: (2026)
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
by: Gu, Shuqi, et al.
Published: (2026)
by: Gu, Shuqi, et al.
Published: (2026)
G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes
by: Xiong, Hong, et al.
Published: (2024)
by: Xiong, Hong, et al.
Published: (2024)
ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
by: Sauter, Andreas, et al.
Published: (2025)
by: Sauter, Andreas, et al.
Published: (2025)
Similar Items
-
A Meta-Learning Perspective on Transformers for Causal Language Modeling
by: Wu, Xinbo, et al.
Published: (2023) -
Online Reinforcement Learning with Passive Memory
by: Pattanaik, Anay, et al.
Published: (2024) -
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
by: Hartman, Max, et al.
Published: (2025) -
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
by: Garriga, Tomàs, et al.
Published: (2026) -
Federated Learning via Lattice Joint Source-Channel Coding
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)