:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Saki, Amir, Faghihi, Usef
Format:	Preprint
Published:	2026
Subjects:	Methodology Artificial Intelligence
Online Access:	https://arxiv.org/abs/2603.14169
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting
by: Faghihi, Usef, et al.
Published: (2026)

Probabilistic Easy Variational Causal Effect
by: Faghihi, Usef, et al.
Published: (2024)

Causal Deep Q Network
by: Khelifi, Elouanes, et al.
Published: (2025)

Probabilistic Variational Causal Approach in Observational Studies
by: Faghihi, Usef, et al.
Published: (2022)

Integrating Fuzzy Logic with Causal Inference: Enhancing the Pearl and Neyman-Rubin Methodologies
by: Saki, Amir, et al.
Published: (2024)

s-ID: Causal Effect Identification in a Sub-Population
by: Abouei, Amir Mohammad, et al.
Published: (2023)

Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
by: Kubota, Kohsuke, et al.
Published: (2025)

Moments of Causal Effects
by: Kawakami, Yuta, et al.
Published: (2025)

On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphs
by: Belciug, Isabela, et al.
Published: (2025)

Constrained Identifiability of Causal Effects
by: Chen, Yizuo, et al.
Published: (2024)

A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs
by: Reisach, Alexander G., et al.
Published: (2026)

Bounding Causal Effects with Leaky Instruments
by: Watson, David S., et al.
Published: (2024)

Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
by: Bhattacharya, Rohit, et al.
Published: (2026)

Identifying Macro Causal Effects in C-DMGs over DMGs
by: Ferreira, Simon, et al.
Published: (2025)

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

Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
by: Murakami, Yuki, et al.
Published: (2025)

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs
by: Ferreira, Simon, et al.
Published: (2024)

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

Transferring Causal Effects using Proxies
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)

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

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

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

Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
by: Ferreira, Simon, et al.
Published: (2024)

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)

Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, 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)

Fast Proxy Experiment Design for Causal Effect Identification
by: Elahi, Sepehr, et al.
Published: (2024)

Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
by: Loranchet, Timothée, et al.
Published: (2025)

Causal reasoning in difference graphs
by: Assaad, Charles K.
Published: (2024)

Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)

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

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

ALCM: Autonomous LLM-Augmented Causal Discovery Framework
by: Khatibi, Elahe, et al.
Published: (2024)

Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
by: Chen, Jianhong, et al.
Published: (2026)

Towards a holistic understanding of Selection Bias for Causal Effect Identification
by: Qiu, Yiwen, et al.
Published: (2026)

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

A Two-Stage Interpretable Matching Framework for Causal Inference
by: Shikalgar, Sahil, et al.
Published: (2025)

Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
by: Wang, Zixiao, et al.
Published: (2023)

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