NeurIPS 2024 Past Causality
NeurIPS 2024 Causal Representation Learning Workshop
CRL@NeurIPS 2024
- Submission deadline
- Oct 2, 2024, 23:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (37)
Fetched from OpenReview (v2) on 2026-06-10.
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A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning
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A Novel Application of SCMs to Time Series Counterfactual Estimation in the Pharmaceutical Industry
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A Shadow Variable Approach to Causal Decision Making with One-sided Feedback
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Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis
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Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
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Causal Inference under Differential Privacy: Challenges and Mitigation Strategies
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Causal Order Discovery based on Monotonic SCMs
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Causal Representation Learning for Cross-Patient Seizure Classification
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Causal Retrieval with Semantic Consideration
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CSRec: Rethinking Sequential Recommendation from A Causal Perspective.
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DAG-aware Transformer for Causal Effect Estimation
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Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data
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Differentiable Causal Discovery for Latent Hierarchical Causal Models
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Estimating Treatment Effect across Heterogeneous Data Sources: An Instrumental Variable Approach
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From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding
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General Causal Imputation via Synthetic Interventions
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Improving Causal Transplant Outcomes through Dynamic Organ Offer Estimation
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Interaction Asymmetry: A General Principle for Learning Composable Abstractions
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Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
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Leveraging a Simulator for Learning Causal Representations for CATE from Post-Treatment Covariates
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LLMs as Emotion Analyzers for Causal Models: Partial Identification with Fuzzy Interval Data
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MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
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On Domain Generalization Datasets as Proxy Benchmarks for Causal Representation Learning
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On the role of prognostic factors and effect modifiers in structural causal models
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Pilot Analysis for: Learning to Encode Multi-level Dynamics in Effect Heterogeneity Estimation
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Robust Domain Generalisation with Causal Invariant Bayesian Neural Networks
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Robust Multi-view Co-expression Network Inference
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Score-Based Interaction Testing in Pairwise Experiments
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Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
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Spectral Representation for Causal Estimation with Hidden Confounders
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Systems with Switching Causal Relations: A Meta-Causal Perspective
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Teaching Invariance Using Privileged Mediation Information
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Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
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Uncovering Latent Causal Structures from Spatiotemporal Data
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Unifying Causal Representation Learning with the Invariance Principle
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Unsupervised Causal Abstraction
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Zero-Shot Learning of Causal Models