#Causal Inference
Latest news articles tagged with "Causal Inference". Follow the timeline of events, related topics, and entities.
Articles (15)
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🇺🇸 CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
[USA]
arXiv:2603.18736v1 Announce Type: cross Abstract: Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on exper...
Related: #AI Alignment -
🇺🇸 Teleological Inference in Structural Causal Models via Intentional Interventions
[USA]
arXiv:2603.18968v1 Announce Type: new Abstract: Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and a...
Related: #Artificial Intelligence -
🇺🇸 Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
[USA]
arXiv:2508.21742v2 Announce Type: replace Abstract: Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structu...
Related: #Time Series Analysis -
🇺🇸 HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding
[USA]
arXiv:2603.12305v1 Announce Type: cross Abstract: The ability to understand and reason about cause and effect -- encompassing interventions, counterfactuals, and underlying mechanisms -- is a corners...
Related: #AI Research -
🇺🇸 Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
[USA]
arXiv:2603.09145v1 Announce Type: cross Abstract: Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, ...
Related: #Machine Learning -
🇺🇸 Causally Robust Reward Learning from Reason-Augmented Preference Feedback
[USA]
arXiv:2603.04861v1 Announce Type: new Abstract: Preference-based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especi...
Related: #AI Alignment -
🇺🇸 Causal Identification from Counterfactual Data: Completeness and Bounding Results
[USA]
arXiv:2602.23541v1 Announce Type: new Abstract: Previous work establishing completeness results for $\textit{counterfactual identification}$ has been circumscribed to the setting where the input data...
Related: #Machine Learning Theory, #Counterfactual Reasoning -
🇺🇸 Causal Direction from Convergence Time: Faster Training in the True Causal Direction
[USA]
arXiv:2602.22254v1 Announce Type: cross Abstract: We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics in which one neural...
Related: #Machine Learning, #Optimization Dynamics -
🇺🇸 CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
[USA]
arXiv:2602.20571v1 Announce Type: new Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect ...
Related: #Artificial Intelligence, #Benchmarking, #Research Evaluation -
🇺🇸 Predicting Subway Passenger Flows under Incident Situation with Causality
[USA]
arXiv:2412.06871v2 Announce Type: replace-cross Abstract: In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal c...
Related: #Machine Learning, #Transportation Systems, #Public Safety -
🇺🇸 Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
[USA]
arXiv:2602.16435v1 Announce Type: new Abstract: Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing ...
Related: #Artificial Intelligence, #Feature Engineering, #Reinforcement Learning, #Robustness to Distribution Shift -
🇺🇸 Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure
[USA]
arXiv:2602.08783v1 Announce Type: new Abstract: Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate comput...
Related: #Artificial Intelligence, #Machine Learning -
🇺🇸 IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
[USA]
arXiv:2602.07943v1 Announce Type: new Abstract: In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of th...
Related: #Artificial Intelligence, #Data Science -
🇺🇸 Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
[USA]
arXiv:2602.07765v1 Announce Type: new Abstract: Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenge...
Related: #Data Science, #Artificial Intelligence -
🇺🇸 Can Post-Training Transform LLMs into Causal Reasoners?
[USA]
arXiv:2602.06337v1 Announce Type: cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in this do...
Related: #Artificial Intelligence, #Machine Learning
Key Entities (4)
- AI alignment (2 news)
- Machine learning (1 news)
- Reinforcement learning from human feedback (1 news)
- Neural network (1 news)
About the topic: Causal Inference
The topic "Causal Inference" aggregates 15+ news articles from various countries.