The field of causal inference and forecasting is experiencing significant growth, driven by innovative applications of machine learning and artificial intelligence. Recent developments have focused on improving the accuracy and interpretability of causal models, as well as enhancing forecasting capabilities in complex real-world environments. Notable advancements include the integration of generative models with structural causal models, enabling high-fidelity counterfactual reasoning and forecasting. Additionally, new frameworks have been proposed for interactive time series forecasting, combining human wisdom with large language model intelligence to achieve state-of-the-art predictive accuracy.

Noteworthy papers include: The Causal Round Trip, which introduces a novel diffusion-based framework for faithful abduction and counterfactual reasoning. AlphaCast, a human wisdom-large language model intelligence co-reasoning framework for interactive time series forecasting, consistently outperforms state-of-the-art baselines in predictive accuracy. AIA Forecaster, a Large Language Model-based system for judgmental forecasting, achieves performance equal to human superforecasters and provides practical recommendations for future research.