This paper investigates implicit causal graph construction from text by inferring intermediate causal events using Large Language Models (LLMs). It compares end-to-end graph construction with causal chain discovery methods and evaluates the validity of inferred causal relations against a manually curated database.
This article details a developer's epiphany while debugging a black-box Reinforcement Learning agent failing to synchronize smart agriculture microgrids. The realization that the agent lacked causal understanding led to exploring Explainable AI and causal inference frameworks to prevent cascading power failures.
This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States, using a range of off-policy evaluation methods. The impact estimates remain consistent in magnitude and statistically significant, confirming original findings.
This paper posits that within-person variability in human outcomes stems from a dynamic latent state within the individual. It argues that human outcomes are controllable through interventions targeting this state and its weighting during decision formation.
This work explores methods for neural models to learn cause-and-effect relationships, even in scenarios where data-generating interventions are unknown. The research aims to enhance artificial intelligence's ability to infer causality from complex data.
This research addresses challenges in continuous-time causal inference due to hidden confounders, demonstrating that observability of latent dynamics is crucial for identifying dynamic treatment effects. It proposes Observable Neural ODEs (ObsNODEs), a novel model for causal forecasting by learning reconstructible continuous-time dynamics.
O artigo apresenta PRISM, uma estrutura para Reinforcement Learning que fundamenta as decisões de agentes em conceitos discretos e causalmente validados, usando-os como interface de transferência zero-shot. Ele demonstra que esses conceitos impulsionam diretamente o comportamento do agente e que a importância de um conceito pode ser dissociada de sua frequência de uso.