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Browsing by Subject "Causal Inference"
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Item Count-Regression-Based Empirical Causal Analysis from a Potential Outcomes Perspective: Accounting for Boundedness, Discreteness, Dispersion and Unobservable Confounding(2024-06) Kazeminezhad, Golnoush; Terza, Joseph V.; Harle, Christopher A.; Morrison, Wendy; Russell, StevenEmpirical economic research is primarily driven by the desire to offer scientific evidence that serves to inform the study of cause-and-effect. In this dissertation, I developed new models for count-regression-model-based (CRM-based) causal effect estimation in which the value for the outcome of interest is restricted to the non-negative integers. I implement first-order two-stage residual inclusion (FO-2SRI) methods, in the context of the general potential outcomes framework, that accommodate nonlinearities due to the intrinsic characteristics of count-valued outcomes such as boundedness (outcome nonnegative), discreteness (outcome has countable support) and dispersion (conditional variance and other higher order conditional moments of the outcome not necessarily equal to its conditional mean) of count data, and unobservable confounding. The focus here is on the case in which the causal variable is continuous. The newly proposed causal effect estimators are compared with extant FO-2SRI estimators based on conventional control function methods and the linear instrumental variables (LIV) estimator. A series of simulation studies are performed to investigate the accuracy of the proposed estimators and compare the results with the extant estimators. In the simulation studies, the robustness of the fully nonlinear CRM-based FO-2SRI methods are investigated with attention to an important type of misspecification error. The models are also applied to a real-world data from Nigeria to investigate the effect of female education on their fertility decisions in a developing country. The results of the simulation studies reveal that estimates obtained via the newly proposed estimators are very accurate and widely diverge from the results from the extant control function and LIV methods. Moreover, one of the new estimators, which allows dispersion flexibility, dominated all other estimators (aside from a few extreme dispersion cases) with regard to avoidance of misspecification bias. Finally, the results showed that same estimator to be quite accurate for a wide range of values of the dispersion parameter (which measures mean/variance divergence). Similar results were obtained via the real data analysis which indicates that increasing women’s education decreases childbearing.Item Robust Inference for Heterogeneous Treatment Effects With Applications to NHANES Data(2024-12) Mo, Ran; Wang, Honglang; Li, Fang; Tan, Fei; Peng, HanxiangEstimating the conditional average treatment effect (CATE) using data from the National Health and Nutrition Examination Survey (NHANES) provides valuable insights into the heterogeneous impacts of health interventions across diverse populations, facilitating public health strategies that consider individual differences in health behaviors and conditions. However, estimating CATE with NHANES data face challenges often encountered in observational studies, such as outliers, heavy-tailed error distributions, skewed data, model misspecification, and the curse of dimensionality. To address these challenges, this dissertation presents three consecutive studies that thoroughly explore robust methods for estimating heterogeneous treatment effects. The first study introduces an outlier-resistant estimation method by incorporating M-estimation, replacing the \(L_2\) loss in the traditional inverse propensity weighting (IPW) method with a robust loss function. To assess the robustness of our approach, we investigate its influence function and breakdown point. Additionally, we derive the asymptotic properties of the proposed estimator, enabling valid inference for the proposed outlier-resistant estimator of CATE. The method proposed in the first study relies on a symmetric assumption which is commonly required by standard outlier-resistant methods. To remove this assumption while maintaining unbiasedness, the second study employs the adaptive Huber loss, which dynamically adjusts the robustification parameter based on the sample size to achieve optimal tradeoff between bias and robustness. The robustification parameter is explicitly derived from theoretical results, making it unnecessary to rely on time-consuming data-driven methods for its selection. We also derive concentration and Berry-Esseen inequalities to precisely quantify the convergence rates as well as finite sample performance. In both previous studies, the propensity scores were estimated parametrically, which is sensitive to model misspecification issues. The third study extends the robust estimator from our first project by plugging in a kernel-based nonparametric estimation of the propensity score with sufficient dimension reduction (SDR). Specifically, we adopt a robust minimum average variance estimation (rMAVE) for the central mean space under the potential outcome framework. Together with higher-order kernels, the resulting CATE estimation gains enhanced efficiency. In all three studies, the theoretical results are derived, and confidence intervals are constructed for inference based on these findings. The properties of the proposed estimators are verified through extensive simulations. Additionally, applying these methods to NHANES data validates the estimators' ability to handle diverse and contaminated datasets, further demonstrating their effectiveness in real-world scenarios.Item Trustworthy AI: Ensuring Explainability & Acceptance(2023-12) Kaur, Davinder; Durresi, Arjan; Tuceryan, Mihran; Dundar, Murat; Hu, QinIn the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory. A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security. The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with an exploration of quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms. In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.