pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning algorithms to predict stroke in China based on causal inference of time series analysis
Authors: Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong
Date: 2025-03-10 00:00:00
Tasks: Causal Inference, Feature Importance, Time Series, Time Series Analysis

Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). Among all the models, the Gradient Boosting model demonstrated the highest performance and stability. Model explanation and feature importance analysis generated model interpretation that illustrated significant contributors associated with risks of stroke. Conclusions and Relevance: This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. The research results indicate that dynamic causal inference features have important value in predicting stroke risk, especially in capturing the impact of changes in health status over time on stroke risk. By further optimizing the model and introducing more variables, this study provides theoretical basis and practical guidance for future stroke prevention and intervention strategies.
Determining a credit transition matrix from cumulative default probabilities
Authors: Henryk Gzyl, Silvia Mayoral
Date: 2025-03-18 00:00:00

To quantify the changes in the credit rating of a bond is an important mathematical problem for the credit rating industry. To think of the credit rating as the state a Markov chain is an interesting proposal leading to challenges in mathematical modeling. Since cumulative default rates are more readily measurable than credit migrations, a natural question is whether the credit transition matrix (CTM) can be determined from the knowledge of the cumulative default probabilities. Here we use a connection between the CTM and the cumulative default probabilities to setup an ill-posed, linear inverse problem with box constraints, which we solve by an entropy minimization procedure. This approach is interesting on several counts. On the one hand, we may have less data that unknowns, and on the other hand, even when we have as much data as unknowns, the matrix connecting them may not be invertible, which makes the problem ill-posed. Besides developing the tools to solve the problem, we apply it to several test cases to check the performance of the method. The results are quite satisfactory.
The fundamental representation of pricing adjustments
Authors: Benedict Burnett, Ryan McCrickerd, Benjamin Piau
Date: 2025-03-19 00:00:00

This article consolidates and extends past work on derivative pricing adjustments, including XVA, by providing an encapsulating representation of the adjustment between any two derivative pricing functions, within an Ito SDE/parabolic PDE framework. We give examples of this representation encapsulating others from the past 20 years, ranging from a well known option pricing adjustment introduced by Gatheral, to the collection of semi-replication XVA originating from Burgard & Kjaer. To highlight extensions, we discuss certain meta-adjustments beyond XVA, designed to help signal and mitigate XVA model risk.
Stochastic Volatility Model with Sticky Drawdown and Drawup Processes: A Deep Learning Approach
Authors: Yuhao Liu, Pingping Jiang, Gongqiu Zhang
Date: 2025-03-19 00:00:00

We propose a new financial model, the stochastic volatility model with sticky drawdown and drawup processes (SVSDU model), which enables us to capture the features of winning and losing streaks that are common across financial markets but can not be captured simultaneously by the existing financial models. Moreover, the SVSDU model retains the advantages of the stochastic volatility models. Since there are not closed-form option pricing formulas under the SVSDU model and the existing simulation methods for the sticky diffusion processes are really time-consuming, we develop a deep neural network to solve the corresponding high-dimensional parametric partial differential equation (PDE), where the solution to the PDE is the pricing function of a European option according to the Feynman-Kac Theorem, and validate the accuracy and efficiency of our deep learning approach. We also propose a novel calibration framework for our model, and demonstrate the calibration performances of our models on both simulated data and historical data. The calibration results on SPX option data show that the SVSDU model is a good representation of the asset value dynamic, and both winning and losing streaks are accounted for in option values. Our model opens new horizons for modeling and predicting the dynamics of asset prices in financial markets.
Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels
Authors: Neal Batra
Date: 2025-03-19 00:00:00

This study explores the application of Hawkes processes to model high-frequency data in the context of limit order books. Two distinct Hawkes-based models are proposed and analyzed: one utilizing exponential kernels and the other employing power-law kernels. These models are implemented within a bivariate framework. The performance of each model is evaluated using high-frequency trading data, with a focus on their ability to reproduce key statistical properties of limit order books. Through a comprehensive comparison, we identify the strengths and limitations of each kernel type, providing insights into their suitability for modeling high-frequency financial data. Simulations are conducted to validate the models, and the results are interpreted. Based on these insights, a trading strategy is formulated.
Systemic Risk Management via Maximum Independent Set in Extremal Dependence Networks
Authors: Qian Hui, Tiandong Wang
Date: 2025-03-03 00:00:00
Tasks: Management

The failure of key financial institutions may accelerate risk contagion due to their interconnections within the system. In this paper, we propose a robust portfolio strategy to mitigate systemic risks during extreme events. We use the stock returns of key financial institutions as an indicator of their performance, apply extreme value theory to assess the extremal dependence among stocks of financial institutions, and construct a network model based on a threshold approach that captures extremal dependence. Our analysis reveals different dependence structures in the Chinese and U.S. financial systems. By applying the maximum independent set (MIS) from graph theory, we identify a subset of institutions with minimal extremal dependence, facilitating the construction of diversified portfolios resilient to risk contagion. We also compare the performance of our proposed portfolios with that of the market portfolios in the two economies.
Robust distortion risk measures with linear penalty under distribution uncertainty
Authors: Yuxin Du, Dejian Tian, HUI ZHANG
Date: 2025-03-20 00:00:00

The paper investigates the robust distortion risk measure with linear penalty function under distribution uncertainty. The distribution uncertainties are characterized by predetermined moment conditions or constraints on the Wasserstein distance. The optimal quantile distribution and the optimal value function are explicitly characterized. Our results partially extend the results of Bernard, Pesenti and Vanduffel (2024) and Li (2018) to robust distortion risk measures with linear penalty. In addition, we also discuss the influence of the penalty parameter on the optimal solution.
Model Risk Management for Generative AI In Financial Institutions
Authors: Anwesha Bhattacharyya, Ye Yu, Hanyu Yang, Rahul Singh, Tarun Joshi, Jie Chen, Kiran Yalavarthy
Date: 2025-03-19 00:00:00
Tasks: Management

The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new model risks, primarily hallucinations and toxicity. As highly regulated entities, financial enterprises (primarily large US banks) are obligated to enhance their model risk framework with additional testing and controls to ensure safe deployment of such applications. This paper outlines the key aspects for model risk management of generative AI model with a special emphasis on additional practices required in model validation.
Active Learning For Repairable Hardware Systems With Partial Coverage
Authors: Michael Potter, Beyza Kalkanli, Deniz Erdoğmuş, Michael Everett
Date: 2025-03-20 00:00:00
Tasks: Active Learning, Diagnostic

Identifying the optimal diagnostic test and hardware system instance to infer reliability characteristics using field data is challenging, especially when constrained by fixed budgets and minimal maintenance cycles. Active Learning (AL) has shown promise for parameter inference with limited data and budget constraints in machine learning/deep learning tasks. However, AL for reliability model parameter inference remains underexplored for repairable hardware systems. It requires specialized AL Acquisition Functions (AFs) that consider hardware aging and the fact that a hardware system consists of multiple sub-systems, which may undergo only partial testing during a given diagnostic test. To address these challenges, we propose a relaxed Mixed Integer Semidefinite Program (MISDP) AL AF that incorporates Diagnostic Coverage (DC), Fisher Information Matrices (FIMs), and diagnostic testing budgets. Furthermore, we design empirical-based simulation experiments focusing on two diagnostic testing scenarios: (1) partial tests of a hardware system with overlapping subsystem coverage, and (2) partial tests where one diagnostic test fully subsumes the subsystem coverage of another. We evaluate our proposed approach against the most widely used AL AF in the literature (entropy), as well as several intuitive AL AFs tailored for reliability model parameter inference. Our proposed AF ranked best on average among the alternative AFs across 6,000 experimental configurations, with respect to Area Under the Curve (AUC) of the Absolute Total Expected Event Error (ATEER) and Mean Squared Error (MSE) curves, with statistical significance calculated at a 0.05 alpha level using a Friedman hypothesis test.
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
Authors: Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
Date: 2025-03-19 00:00:00

The performance of sequential Monte Carlo (SMC) samplers heavily depends on the tuning of the Markov kernels used in the path proposal. For SMC samplers with unadjusted Markov kernels, standard tuning objectives, such as the Metropolis-Hastings acceptance rate or the expected-squared jump distance, are no longer applicable. While stochastic gradient-based end-to-end optimization has been explored for tuning SMC samplers, they often incur excessive training costs, even for tuning just the kernel step sizes. In this work, we propose a general adaptation framework for tuning the Markov kernels in SMC samplers by minimizing the incremental Kullback-Leibler (KL) divergence between the proposal and target paths. For step size tuning, we provide a gradient- and tuning-free algorithm that is generally applicable for kernels such as Langevin Monte Carlo (LMC). We further demonstrate the utility of our approach by providing a tailored scheme for tuning kinetic LMC used in SMC samplers. Our implementations are able to obtain a full schedule of tuned parameters at the cost of a few vanilla SMC runs, which is a fraction of gradient-based approaches.
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts
Authors: Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
Date: 2025-03-19 00:00:00
Tasks: Causal Discovery

Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well characterized. One of the most popular methods is to evaluate the stability of model features after resampling the data, similar to resampling methods for estimating confidence intervals in statistics. Many aspects of this approach have received little to no attention, however, such as whether the choice of resampling method should depend on the sample size, algorithms being used, or algorithm tuning parameters. We present theoretical results proving that certain resampling methods closely emulate the assignment of specific values to algorithm tuning parameters. We also report the results of extensive simulation experiments, which verify the theoretical result and provide substantial data to aid researchers in further characterizing resampling in the context of causal discovery analysis. Together, the theoretical work and simulation results provide specific guidance on how resampling methods and tuning parameters should be selected in practice.
The Hardness of Validating Observational Studies with Experimental Data
Authors: Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee
Date: 2025-03-19 00:00:00

Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attempt to remove this bias by supplementing observational data with experimental data, which, when available, is typically on a smaller scale due to the time and cost involved in running a randomised controlled trial. In this work, we prove a theorem that places fundamental limits on this ``best of both worlds'' approach. Using the framework of impossible inference, we show that although it is possible to use experimental data to \emph{falsify} causal effect estimates from observational data, in general it is not possible to \emph{validate} such estimates. Our theorem proves that while experimental data can be used to detect bias in observational studies, without additional assumptions on the smoothness of the correction function, it can not be used to remove it. We provide a practical example of such an assumption, developing a novel Gaussian Process based approach to construct intervals which contain the true treatment effect with high probability, both inside and outside of the support of the experimental data. We demonstrate our methodology on both simulated and semi-synthetic datasets and make the \href{https://github.com/Jakefawkes/Obs_and_exp_data}{code available}.
How Well Can AI Build SD Models?
Authors: William Schoenberg, Davidson Girard, Saras Chung, Ellen O'Neill, Janet Velasquez, Sara Metcalf
Date: 2025-03-19 00:00:00
Tasks: Translation

Introduction: As system dynamics (SD) embraces automation, AI offers efficiency but risks bias from missing data and flawed models. Models that omit multiple perspectives and data threaten model quality, whether created by humans or with the assistance of AI. To reduce uncertainty about how well AI can build SD models, we introduce two metrics for evaluation of AI-generated causal maps: technical correctness (causal translation) and adherence to instructions (conformance). Approach: We developed an open source project called sd-ai to provide a basis for collaboration in the SD community, aiming to fully harness the potential of AI based tools like ChatGPT for dynamic modeling. Additionally, we created an evaluation theory along with a comprehensive suite of tests designed to evaluate any such tools developed within the sd-ai ecosystem. Results: We tested 11 different LLMs on their ability to do causal translation as well as conform to user instruction. gpt-4.5-preview was the top performer, scoring 92.9% overall, excelling in both tasks. o1 scored 100% in causal translation. gpt-4o identified all causal links but struggled with positive polarity in decreasing terms. While gpt-4.5-preview and o1 are most accurate, gpt-4o is the cheapest. Discussion: Causal translation and conformance tests applied to the sd-ai engine reveal significant variations across lLLMs, underscoring the need for continued evaluation to ensure responsible development of AI tools for dynamic modeling. To address this, an open collaboration among tool developers, modelers, and stakeholders is launched to standardize measures for evaluating the capacity of AI tools to improve the modeling process.
R$^2$: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs
Authors: Zefeng Lin, Yi Xiao, Zhiqiang Mo, Qifan Zhang, Jie Wang, Jiayang Chen, Jiajing Zhang, HUI ZHANG, Zhengyi Liu, Xianyong Fang, Xiaohua Xu
Date: 2025-03-19 00:00:00
Tasks: graph construction, Hallucination, Text Generation

Automatically adapting novels into screenplays is important for the TV, film, or opera industries to promote products with low costs. The strong performances of large language models (LLMs) in long-text generation call us to propose a LLM based framework Reader-Rewriter (R$^2$) for this task. However, there are two fundamental challenges here. First, the LLM hallucinations may cause inconsistent plot extraction and screenplay generation. Second, the causality-embedded plot lines should be effectively extracted for coherent rewriting. Therefore, two corresponding tactics are proposed: 1) A hallucination-aware refinement method (HAR) to iteratively discover and eliminate the affections of hallucinations; and 2) a causal plot-graph construction method (CPC) based on a greedy cycle-breaking algorithm to efficiently construct plot lines with event causalities. Recruiting those efficient techniques, R$^2$ utilizes two modules to mimic the human screenplay rewriting process: The Reader module adopts a sliding window and CPC to build the causal plot graphs, while the Rewriter module generates first the scene outlines based on the graphs and then the screenplays. HAR is integrated into both modules for accurate inferences of LLMs. Experimental results demonstrate the superiority of R$^2$, which substantially outperforms three existing approaches (51.3%, 22.6%, and 57.1% absolute increases) in pairwise comparison at the overall win rate for GPT-4o.
Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat
Authors: Joseph Emmanuel DL Dayo, Michel Onasis S. Ogbinar, Prospero C. Naval Jr
Date: 2025-03-19 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL)

The objective of this study is to design and implement a reinforcement learning (RL) environment using D\&D 5E combat scenarios to challenge smaller RL agents through interaction with a robust adversarial agent controlled by advanced Large Language Models (LLMs) like GPT-4o and LLaMA 3 8B. This research employs Deep Q-Networks (DQN) for the smaller agents, creating a testbed for strategic AI development that also serves as an educational tool by simulating dynamic and unpredictable combat scenarios. We successfully integrated sophisticated language models into the RL framework, enhancing strategic decision-making processes. Our results indicate that while RL agents generally outperform LLM-controlled adversaries in standard metrics, the strategic depth provided by LLMs significantly enhances the overall AI capabilities in this complex, rule-based setting. The novelty of our approach and its implications for mastering intricate environments and developing adaptive strategies are discussed, alongside potential innovations in AI-driven interactive simulations. This paper aims to demonstrate how integrating LLMs can create more robust and adaptable AI systems, providing valuable insights for further research and educational applications.
ECLAIR: Enhanced Clarification for Interactive Responses
Authors: John Murzaku, Zifan Liu, Md Mehrab Tanjim, Vaishnavi Muppala, Xiang Chen, Yunyao Li
Date: 2025-03-19 00:00:00
Tasks: Question Generation, Question-Generation

We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR generates clarification questions for ambiguous user queries and resolves ambiguity based on the user's response.We introduce a generalized architecture capable of integrating ambiguity information from multiple downstream agents, enhancing context-awareness in resolving ambiguities and allowing enterprise specific definition of agents. We further define agents within our system that provide domain-specific grounding information. We conduct experiments comparing ECLAIR to few-shot prompting techniques and demonstrate ECLAIR's superior performance in clarification question generation and ambiguity resolution.
Using Language Models to Decipher the Motivation Behind Human Behaviors
Authors: Yutong Xie, Qiaozhu Mei, Walter Yuan, Matthew O. Jackson
Date: 2025-03-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

AI presents a novel tool for deciphering the motivations behind human behaviors. By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we infer (decipher) the motivations behind the human behaviors. We also show how one can analyze the prompts to reveal relationships between the classic economic games, providing insight into what different economic scenarios induce people to think about. We also show how this deciphering process can be used to understand differences in the behavioral tendencies of different populations. We show how AI offers a new way to examine the thinking and framing that produce different behaviors.
Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
Authors: Elena Malnatsky, ShengHui Wang, Koen V. Hindriks, Mike E. U. Ligthart
Date: 2025-03-20 00:00:00

Dialogic learning fosters motivation and deeper understanding in education through purposeful and structured dialogues. Foundational models offer a transformative potential for child-robot interactions, enabling the design of personalized, engaging, and scalable interactions. However, their integration into educational contexts presents challenges in terms of ensuring age-appropriate and safe content and alignment with pedagogical goals. We introduce a hybrid approach to designing personalized educational dialogues in child-robot interactions. By combining rule-based systems with LLMs for selective offline content generation and human validation, the framework ensures educational quality and developmental appropriateness. We illustrate this approach through a project aimed at enhancing reading motivation, in which a robot facilitated book-related dialogues.