Identifying Conditional Causal Effects in MPDAGs
Fuente:
arXiv
Saved in:
| Main Authors: | LaPlante, Sara, Perković, Emilija |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Conditional Adjustment in a Markov Equivalence Class
by: LaPlante, Sara, et al.
Published: (2023)
by: LaPlante, Sara, et al.
Published: (2023)
Data-Driven Adjustment for Multiple Treatments
by: LaPlante, Sara, et al.
Published: (2025)
by: LaPlante, Sara, et al.
Published: (2025)
Towards Complete Causal Explanation with Expert Knowledge
by: Venkateswaran, Aparajithan, et al.
Published: (2024)
by: Venkateswaran, Aparajithan, et al.
Published: (2024)
Lost in Aggregation: The Causal Interpretation of the IV Estimand
by: Tsao, Danielle, et al.
Published: (2026)
by: Tsao, Danielle, 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 Causal Effects Under Functional Dependencies
by: Chen, Yizuo, et al.
Published: (2024)
by: Chen, Yizuo, et al.
Published: (2024)
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
by: Rahman, Md Musfiqur, et al.
Published: (2024)
by: Rahman, Md Musfiqur, et al.
Published: (2024)
Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)
by: Massidda, Riccardo, et al.
Published: (2024)
Causal Layering via Conditional Entropy
by: Feigenbaum, Itai, et al.
Published: (2024)
by: Feigenbaum, Itai, et al.
Published: (2024)
Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)
by: Yin, Naiyu, et al.
Published: (2023)
Causal Discovery with Fewer Conditional Independence Tests
by: Shiragur, Kirankumar, et al.
Published: (2024)
by: Shiragur, Kirankumar, et al.
Published: (2024)
On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
by: Monés, Marc Franquesa, et al.
Published: (2026)
by: Monés, Marc Franquesa, et al.
Published: (2026)
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
by: Zhang, Jiaru, et al.
Published: (2025)
by: Zhang, Jiaru, et al.
Published: (2025)
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
by: Xie, Feng, et al.
Published: (2023)
by: Xie, Feng, et al.
Published: (2023)
Transferring Causal Effects using Proxies
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)
Compositional Models for Estimating Causal Effects
by: Pruthi, Purva, et al.
Published: (2024)
by: Pruthi, Purva, et al.
Published: (2024)
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
by: Jeong, Hyunchai, et al.
Published: (2024)
by: Jeong, Hyunchai, et al.
Published: (2024)
Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, et al.
Published: (2024)
by: Schröder, Maresa, et al.
Published: (2024)
The Estimation of Continual Causal Effect for Dataset Shifting Streams
by: Chen, Baining, et al.
Published: (2025)
by: Chen, Baining, et al.
Published: (2025)
s-ID: Causal Effect Identification in a Sub-Population
by: Abouei, Amir Mohammad, et al.
Published: (2023)
by: Abouei, Amir Mohammad, et al.
Published: (2023)
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
by: Reddy, Abbavaram Gowtham, et al.
Published: (2022)
by: Reddy, Abbavaram Gowtham, et al.
Published: (2022)
Fast Proxy Experiment Design for Causal Effect Identification
by: Elahi, Sepehr, et al.
Published: (2024)
by: Elahi, Sepehr, et al.
Published: (2024)
A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)
by: Yin, Yujia, et al.
Published: (2025)
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
by: Mahajan, Divyat, et al.
Published: (2022)
by: Mahajan, Divyat, et al.
Published: (2022)
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
by: Chen, Tianyu, et al.
Published: (2023)
by: Chen, Tianyu, et al.
Published: (2023)
Towards a holistic understanding of Selection Bias for Causal Effect Identification
by: Qiu, Yiwen, et al.
Published: (2026)
by: Qiu, Yiwen, et al.
Published: (2026)
Multi-Domain Causal Discovery in Bijective Causal Models
by: Jalaldoust, Kasra, et al.
Published: (2025)
by: Jalaldoust, Kasra, et al.
Published: (2025)
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
by: Ng, Woon Yee, et al.
Published: (2025)
by: Ng, Woon Yee, et al.
Published: (2025)
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
by: Gkountouras, John, et al.
Published: (2024)
by: Gkountouras, John, et al.
Published: (2024)
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
by: Zhang, Jiaqi, et al.
Published: (2023)
by: Zhang, Jiaqi, et al.
Published: (2023)
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
by: Liu, Xiaohui, et al.
Published: (2022)
by: Liu, Xiaohui, et al.
Published: (2022)
Causal Preference Elicitation
by: Bonilla, Edwin V., et al.
Published: (2026)
by: Bonilla, Edwin V., et al.
Published: (2026)
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2026)
by: Yi, Huiyang, et al.
Published: (2026)
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
by: Takayama, Masayuki, et al.
Published: (2024)
by: Takayama, Masayuki, 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)
Causal vs. Anticausal merging of predictors
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
Causal Identification in Time Series Models
by: Jahn, Erik, et al.
Published: (2025)
by: Jahn, Erik, et al.
Published: (2025)
Marginal Causal Flows for Validation and Inference
by: Manela, Daniel de Vassimon, et al.
Published: (2024)
by: Manela, Daniel de Vassimon, et al.
Published: (2024)
Partially Observed Structural Causal Models
by: Orujlu, Turan, et al.
Published: (2026)
by: Orujlu, Turan, et al.
Published: (2026)
Similar Items
-
Conditional Adjustment in a Markov Equivalence Class
by: LaPlante, Sara, et al.
Published: (2023) -
Data-Driven Adjustment for Multiple Treatments
by: LaPlante, Sara, et al.
Published: (2025) -
Towards Complete Causal Explanation with Expert Knowledge
by: Venkateswaran, Aparajithan, et al.
Published: (2024) -
Lost in Aggregation: The Causal Interpretation of the IV Estimand
by: Tsao, Danielle, et al.
Published: (2026) -
On the Granularity of Causal Effect Identifiability
by: Chen, Yizuo, et al.
Published: (2025)