Learning decision policies from confounded observational data is a challenging task in causal inference, as unobserved confounders can lead to biased or suboptimal actions when relying solely on machine learning models. A synergistic approach is learning to defer, which decides when to act itself and when to defer to a human expert with access to unobserved information. However, constructing the learning target, which defines the probability of choosing each action or deferral, remains a core challenge. To address this, we propose causal-target-based learning to defer (CTLD) framework, where the causal target is constructed from sharp bounds on potential outcomes. Specifically, the degree of overlap between these bounds determines the probability of deferral, while their relative positions and widths define the probabilities over actions. CTLD aligns model predictions with this causal target to make probabilistic decisions over actions and deferral. We present comprehensive theoretical guarantees for the learned policy and demonstrate the effectiveness of CTLD on synthetic and semi-synthetic datasets.

Using Web Mining and Content Analysis to find and understand clues from the massive amount of unstructured text online is very important for predicting future events and providing early risk warnings in important fields like finance and public safety. While Large Language Models (LLMs) exhibit potential in processing and understanding text, current text-based event prediction faces two primary challenges: first, an insufficient utilization of potential information within the text, such as causal relationships and latent associations, and second, limited predictive reliability constrained by issues like the LLM's own ability and hallucinations. To address these challenges, we propose a novel event prediction framework, Bidirectional Reasoning with Self-Correction (BRSC). BRSC comprises two complementary reasoning dimensions: temporal deductive reasoning, which analyzes the trajectory of historical events along the timeline to enable accurate trend extrapolation, and synchronic associative reasoning, which deeply mines details and latent connections from documents within a specific time window to extended semantic information. In addition, we use a self-correction mechanism that identifies and rectifies potential hallucinations and errors during the reasoning process. Extensive experiments on international relations event prediction demonstrate that BRSC achieves significant improvements over several leading LLM-based methods.

Reinforcement learning, a prominent area in machine learning, excels in executing complex tasks by learning strategies through environmental interaction. Its unique capability to operate without explicit guidance places it at the forefront of advancing autonomous systems. Traditional reinforcement learning approaches, however, face substantial challenges in scenarios with sparse rewards and intricate environmental elements. These limitations manifest in low sample efficiency and a struggle to discern and leverage causal structures in the environment. This not only impedes the learning efficiency but also restricts the broader application of these models in more complex scenarios. This study introduces a causality-driven reinforcement learning (CDRL) framework, that infuses causal knowledge into the actor-critic model. It enhances strategic accuracy by using causal insights for smarter action choices and state evaluations, thus streamlining the learning process. The focus on causal relationships in CDRL aids in more accurate future action predictions and decisions. The methodology of CDRL proves effective in overcoming challenges posed by sparse rewards and complex causal structures in reinforcement learning. It elevates learning efficiency and strategic precision while also improving decision-making transparency and interpretability.

Humanitarian relief operations are often accompanied by regional conflicts around the globe, at risk of deliberate, persistent and unpredictable attacks. However, the long-term channeling of aid resources into conflict areas may influence subsequent patterns of violence and expose local communities to new risks. In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. Specifically, we first model the problem of long-lasting humanitarian relief operations in conflict areas as an online multi-stage rescuer-and-attacker interdiction game in which aid demands are revealed in an online fashion. Both models of single-source and multiple-source relief supply policy are established respectively, and two corresponding near-optimal online algorithms are proposed. In conjunction with a real case of anti-Ebola practice in conflict areas of DR Congo, we find that 1) long-lasting humanitarian relief operations aiming alleviation of crises in conflict areas can lead to indirect funding of local rebel groups; 2) the operations can activate the rebel groups to some extent, as evidenced by the scope expansion of their activities. Furthermore, the impacts of humanitarian aid intensity, frequency and supply policies on the above externalities are quantitatively analyzed, which will provide enlightening decision-making support for the implementation of related operations in the future.

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

Wireless sensor networks (WSNs) are an effective and efficient method for collecting data from a target area, and prolonging the lifetime of WSNs has been a focus of scientific research due to the limited energy of sensor nodes. However, traditional studies of WSNs are based on the assumption that WSNs collect aggregated data with redundant sensor nodes in an ideal radio environment. These assumptions may not be acceptable in practical applications. To address this problem, this paper introduces a novel application scenario for WSNs in which raw data are collected by a multihop network without redundant sensor nodes, and a new hybrid tree-based and cluster-based routing protocol for raw data collection (HTC-RDC) is proposed to prolong the lifetime of WSNs that can work in novel application scenarios. The experimental results demonstrate that the proposed HTC-RDC enables the WSNs to achieve their expected functions and prolongs the network lifetime by an average of 11.4% compared to the existing protocols in the explored application scenario.

We first establish the dynamic Radar Cross Section (RCS) signal model for a conical ballistic missile warhead with precession motion. Scintillation is modeled as a log-normal multiplicative noise. The distribution of the obtained RCS signal is nonGaussian and cannot be obtained in closed-form. Hence, the exact Maximum Likelihood Estimation (MLE) of the pertinent parameter, the missile precession frequency, is untractable. We propose three pseudo MLE approaches. The first approach, called GML, enforces a Gaussian distribution on both the additive and multiplicative noise components. The second approach, called ML8, ignores the additive noise in the measured RCS. The third approach, called AOML, ignores the multiplicative noise. Simulations show that accounting for the multiplicative noise in the estimation significantly improves estimation performance.