Rui Song

To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and further enhance the implementation of causal decision making in practice, with real-world applications illustrated through the proposed causal decision-making workflow. To facilitate broader adoption, we additionally integrate relevant methods into a unified Python-based collection, offering a methodological and practical framework for the community (available at https://causaldm.github.io/Causal-Decision-Making).

Large Language Model (LLM) agents commonly condition actions on full action-observation histories, which introduce task-irrelevant information that easily leads to redundant actions and higher inference cost. We propose Progress-Aware Belief Update (PABU), a belief-state framework that compactly represents an agent's state by explicitly modeling task progress and selectively retaining past actions and observations. At each step, the agent predicts its relative progress since the previous round and decides whether the newly encountered interaction should be stored, conditioning future decisions only on the retained subset. Across eight environments in the AgentGym benchmark, and using identical training trajectories, PABU achieves an 81.0% task completion rate, outperforming previous State of the art (SoTA) models with full-history belief by 23.9%. Additionally, PABU's progress-oriented action selection improves efficiency, reducing the average number of interaction steps to 9.5, corresponding to a 26.9% reduction. Ablation studies show that both explicit progress prediction and selective retention are necessary for robust belief learning and performance gains.

Off-Policy Evaluation (OPE) aims to estimate the value of a target policy using offline data collected from potentially different policies. In real-world applications, however, logged data often suffers from missingness. While OPE has been extensively studied in the literature, a theoretical understanding of how missing data affects OPE results remains unclear. In this paper, we investigate OPE in the presence of monotone missingness and theoretically demonstrate that the value estimates remain unbiased under ignorable missingness but can be biased under nonignorable (informative) missingness. To retain the consistency of value estimation, we propose an inverse probability weighted value estimator and conduct statistical inference to quantify the uncertainty of the estimates. Through a series of numerical experiments, we empirically demonstrate that our proposed estimator yields a more reliable value inference under missing data.

In recent years, there has been a growing trend of applying reinforcement learning (RL) in financial applications. This approach has shown great potential for decision-making tasks in finance. In this review, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared with traditional methods. Moreover, we identify challenges, including explainability, Markov decision process modeling, and robustness, that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL, to address these challenges and to further enhance the implementation of RL in finance.

Each year, a significant portion of the 40 million individuals in the United States who seek care in emergency departments (EDs) following traumatic experiences develop adverse posttraumatic neuropsychiatric sequelae (APNS). This highlights the widespread impact of trauma and the critical need for effective interventions to address the health outcomes of these events. Despite significant research efforts, advancements in understanding the neurobiological mechanisms of APNS have been hindered, primarily due to reliance on subjective self-reports, which are susceptible to recall biases and careless responses. To overcome this limitation, we investigate the use of objective, longitudinal mobile device data to identify consistent APNS states and examine their dynamic transitions over time. To address the complexity of longitudinal mobile data, we developed a continuous-time hidden Markov factor model and applied it to mobile device data from the Advancing Understanding of Recovery after Trauma (AURORA) study. Findings from this study highlight the great potential of leveraging mobile device data for continuous health monitoring and for guiding personalized treatment approaches through mobile health initiatives. A Python implementation of the proposed method is available at https://anonymous.4open.science/r/CTHMFM .