Full Agenda

Below please find the schedule of the CLeaR 2025 conference:

Day 1 (May 7, Wednesday)

• Oral I.1. Algorithmic Syntactic Causal Identification
• Oral I.2. The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference
• Oral I.3. Stabilized Inverse Probability Weighting via Isotonic Calibration

• Poster I.1. Algorithmic Syntactic Causal Identification
• Poster I.2. Contagion Effect Estimation Using Proximal Embeddings
• Poster I.3. Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes
• Poster I.4. Automatic Debiasing of Neural Networks via Moment Constrained Learning
• Poster I.5. Non-Parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
• Poster I.6. Encode-Decoder-Based GAN for Estimating Counterfactual Outcomes Under Sequential Selection Bias and Combinatorial Explosion
• Poster I.7. Interpretable Neural Causal Models with TRAM-DAGs
• Poster I.8. Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
• Poster I.9. Disparate Effect of Missing Mediators on Transportability of Causal Effects
• Poster I.10. Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs
• Poster I.11. Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
• Poster I.12. The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference
• Poster I.13. Probably Approximately Correct High Dimensional Causal Effect Estimation Given a Valid Adjustment Set
• Poster I.14. Stabilized Inverse Probability Weighting via Isotonic Calibration
• Poster I.15. Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation
• Poster I.16. Network Causal Effect Estimation in Graphical Models of Contagion and Latent Confounding
• Poster I.17. Your Assumed DAG is Wrong and Here's How to Deal with It
• Poster I.18. Causal Drivers of Dynamic Networks
• Poster I.19. Compositional Models for Estimating Causal Effects
• Poster I.20. Causal Identification in Time Series Models

• Oral II.1. Causal Bandits Without Graph Learning
• Oral II.2. Combining Causal Models for More Accurate Abstractions of Neural Networks
• Oral II.3. Algorithmic Causal Structure Emerging Through Compression

Day 2 (May 8, Thursday)

• Poster II.1. Fair Clustering: A Causal Perspective
• Poster II.2. Beyond Single-Feature Importance with ICECREAM
• Poster II.3. Actual Causation and Nondeterministic Causal Models
• Poster II.4. Aligning Graphical and Functional Causal Abstractions
• Poster II.5. Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability
• Poster II.6. Causal Bandits Without Graph Learning
• Poster II.7. Counterfactual Influence in Markov Decision Processes
• Poster II.8. Omitted Labels Induce Nontransitive Paradoxes in Causality
• Poster II.9. The Causal-Effect Score in Data Management
• Poster II.10. Inducing Causal Structure Applied to Glucose Prediction for T1DM Patients
• Poster II.11. Combining Causal Models for More Accurate Abstractions of Neural Networks
• Poster II.12. Counterfactual Explanability of Black-Box Prediction Models
• Poster II.13. MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems
• Poster II.14. Counterfactual Token Generation in Large Language Models
• Poster II.15. Algorithmic Causal Structure Emerging Through Compression
• Poster II.16. On Measuring Intrinsic Causal Attributions in Deep Neural Networks
• Poster II.17. Relational Object-Centric Actor-Critic
• Poster II.18. Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents

• Oral III.1. Causal Reasoning in Difference Graphs
• Oral III.2. An Asymmetric Independence Model for Causal Discovery on Path Spaces
• Oral III.3. Scalable Causal Structure Learning via Amortized Conditional Independence Testing

Day 3 (May 9, Friday)

• Poster III.1. Causal Reasoning in Difference Graphs
• Poster III.2. Shapley-PC: Constraint-Based Causal Structure Learning with a Shapley Inspired Framework
• Poster III.3. Robust Multi-View Co-Expression Network Inference
• Poster III.4. The CausalBench Challenge: A Machine Learning Contest for Gene Network Inference from Single-Cell Perturbation Data
• Poster III.5. Score Matching Through the Roof: Linear, Nonlinear, and Latent Variables Causal Discovery
• Poster III.6. Exact Discovery is Polynomial for Certain Sparse Causal Bayesian Networks
• Poster III.7. Cross-Validating Causal Discovery via Leave-One-Variable-Out
• Poster III.8. The Interventional Bayesian Gaussian Equivalent Score for Bayesian Causal Inference with Unknown Soft Interventions
• Poster III.9. The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
• Poster III.10. An Asymmetric Independence Model for Causal Discovery on Path Spaces
• Poster III.11. AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery
• Poster III.12. Controlling for Discrete Unmeasured Confounding in Nonlinear Causal Models
• Poster III.13. Constraint-Based Causal Discovery with Tiered Background Knowledge and Latent Variables in Single or Overlapping Datasets
• Poster III.14. Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
• Poster III.15. Multi-Domain Causal Discovery in Bijective Causal Models
• Poster III.16. Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models
• Poster III.17. Scalable Causal Structure Learning via Amortized Conditional Independence Testing
• Poster III.18. Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before-After Studies: Discovering ADEs in Generics
• Poster III.19. Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery
• Poster III.20. Nondeterministic Causal Models