The field of machine learning and statistical analysis is moving towards developing more robust and reliable methods for uncertainty quantification and causal analysis. Recent research has focused on improving the accuracy and efficiency of these methods, with a particular emphasis on handling complex data distributions and noisy environments. One notable trend is the development of novel uncertainty quantification methods that can provide more informative and calibrated estimates of uncertainty, which is crucial for safe decision-making in high-risk domains. Another area of focus is the development of more robust causal analysis methods that can handle errors and imperfections in the data, which is essential for ensuring the reliability and interpretability of empirical findings. Noteworthy papers in this area include Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows, which introduces a novel post hoc uncertainty quantification method, and Stress-Testing Causal Claims via Cardinality Repairs, which proposes a framework for robustness auditing via cardinality repairs.