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This paper investigates how historical A/B test data can inform adaptive experiments using contextual bandits, providing a practical methodology for deciding when and how to deploy adaptive policies based on offline policy evaluation.
This paper introduces ARGUS, a language-model pipeline for auditing identification assumptions in climate-policy causal evaluations, demonstrating improved flaw detection compared to keyword-based methods.
This paper proposes a method for learning risk scores from observational data that are robust to unobserved confounding, using sensitivity analysis and Wasserstein distributionally robust optimization. The approach improves calibration over traditional benchmarks and state-of-the-art methods.
This paper proposes a method to estimate the causal effect of benchmark exposure on AI model performance, moving beyond traditional overlap techniques for more robust evaluation.
This paper proposes a causal framework for understanding concept drift in data streams using Structural Causal Models, with a taxonomy and generator for simulating and evaluating drift events in non-stationary environments.
This paper establishes that one intervention per strongly connected component suffices to recover parameters of linear stochastic dynamics from steady-state data, and proposes a recursive learning algorithm.
This paper proposes Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that uses causal inference to learn input and output regions where membership is both sufficient and necessary for model predictions, improving explainability.
LimiX-2 is a pretrained foundation model for structured data that uses contextual mechanism networks to achieve #1 on major tabular benchmarks, supporting multiple tasks without task-specific parameter updates.
This paper proposes counterfactual marginalisation as a test-time evaluation framework for assessing the robustness of machine learning models to nuisance variables like demographics in medical image analysis, using counterfactual image generation and prediction averaging.
A free 490-page textbook on causal inference by Peng Ding, covering topics from correlation to longitudinal data with accompanying R code and datasets.
This paper proposes a spillover-aware method for multi-value activation steering to achieve pluralistic alignment in LLMs, improving control over multiple value dimensions without fine-tuning or reward models.
This paper introduces causal foundation models (CFMs), which are pretrained neural networks that estimate causal quantities on new datasets using in-context learning without requiring fine-tuning.
CauseCollab proposes a causal unified and modality-agnostic network to address issues in heterogeneous collaborative perception by disentangling semantic factors from modality-specific confounders, achieving state-of-the-art performance on benchmark datasets.
Simile is building a 'What-If Machine' to simulate real-world decisions and understand the impact of interventions, emphasizing causation over correlation.
The paper evaluates how prediction error of nuisance functions relates to causal estimator performance, finding that prediction error is not a consistent measure of causal bias or confidence interval coverage across methods like XGBoost and Double Machine Learning.
This paper introduces Causal Foundation Models, which use pretrained neural networks to estimate causal effects on new datasets via in-context learning without fine-tuning, providing a practical guide to this emerging field.
This simulation study quantifies selection bias in retail inflation estimation and compares correction methods, finding that stratification generally outperforms inverse probability weighting in long-tail contexts with severe positivity violations.
This paper introduces a causal graph-based attention mechanism to enhance retrieval precision in Retrieval-Augmented Generation (RAG) systems, showing improvements in keyword-stuffing regimes of proprietary knowledge bases.
Introduces CCPL, a method to address delayed and stochastic consequences in constrained reinforcement learning using a delay-corrected Bellman operator and an Interventional Consequence Net for causal attribution, with a contraction proof under unknown delays.
CLS introduces a scalable framework for simultaneous causal network inference and forecasting in dynamical systems, achieving high-fidelity reconstruction and accurate predictions in benchmarks.