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    Bidirectional insights between classical and quantum causal inference
    (University of Waterloo, 2026-08-07) Maciel Ansanelli, Marina
    Causal inference is a sub-area of statistics that investigates the causal explanations underlying observed data. The last decade has seen growing recognition of its applications to quantum physics, which include the use of causal inference to certify quantumness through violations of Bell-like inequalities. This connection places quantum physicists in a unique position to contribute to classical causal inference by addressing open problems whose solutions benefit both fields. This thesis highlights this bidirectional interplay of fields, presenting both the study of foundational problems in classical causal inference from the perspective of quantum physics and the application of these results to quantum research. An important problem in causal inference is attesting when two causal structures are in principle indistinguishable from data obtained under a given probing scheme on the visible variables. For example, passive observations on two variables cannot distinguish between a direct causal relation and a shared latent common cause. However, they {\em can} be distinguished by interventional probing schemes. The first question we address here is which causal structures remain indistinguishable even under the most informative interventional probing schemes. We derive a necessary and sufficient condition for such indistinguishability. We then consider the same question under access to passive observations only, compiling all known sufficient rules for indistinguishability and applying them to causal structures with three and four visible nodes, though a complete characterization remains open. Then, we extend these results to scenarios involving selection bias. The works described above also help identify which causal structures impose nontrivial inequality constraints on their classically realizable distributions; an example is the Bell causal structure, that imposes Bell inequalities. This has particular interest for quantum physicists, because inequality constraints are a prerequisite for a causal structure to have a quantum-classical gap (QC gap), that is, for it to explain more distributions when its latent nodes are associated with quantum systems than when its latent nodes are associated with classical variables. This is important because, when we know that a phenomenon is governed by a causal structure that presents a QC gap, observing a probability distribution over the observed variables that cannot be explained using classical latent variables in that structure certifies that the phenomenon involves genuine nonclassicality. Thus, after identifying which causal structures could potentially exhibit a QC gap, we investigate which actually do. Using techniques developed here, we show that hundreds of new causal structures have a QC gap, while previously only around a dozen examples were known.
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    Dimensionality Testing, Shape-Constrained Neural Network Estimation, and Equilibrium Approximation in Team Games
    (University of Waterloo, 2026-08-07) Zhang, Zetian
    Modern advancements in computational technology have catalyzed an explosion in data dimensionality, enabling researchers to model intricate relationships among multitudinous variables simultaneously; however, these advancements also challenge the application of many useful classical theories (for instance, the $F$-test which is one of the most widely used tests fails with increasing dimensions alongside sample growth). Although some modifications of classical theories, such as the corrected $F$-test, have been proposed to mitigate the high-dimensional bias, a more fundamental question about whether the dimension is ``high" has not been addressed. In Chapter 1, we tackle this problem by proposing a test statistic, which is asymptotically normal, to distinguish high from non-high dimensionality for vectors. In large sample sizes, our test has the correct size when the dimension is ``high"; and it is asymptotically powerful against all alternatives with ``low" dimensions. A set of simulation studies demonstrates that our test achieves satisfactory size and power even in small to moderate sample sizes. Deep neural networks (DNNs), as the most modern advancement, have demonstrated exceptional performance across a wide range of domains, such as image classification \citep{NIPS2012_c399862d,he2016deep}, speech recognition \citep{hinton2012deep}, natural language processing \citep{mikolov2013efficient,vaswani2017attention} and game intelligence \citep{silver2016mastering,perolat2022mastering}. DNNs can be conceptualized as a generalization of classical sieve estimators, transitioning from ``single-layer" to ``multi-layer" architectures. This architectural evolution substantially expands the model's parameter space, but diminishing the interpretability of parameters and the model itself. In Chapter 2, we provide the consistency and asymptotic normality of the DNN-based estimator for point estimation and extend these properties the case with prior knowledge of shapes. The incorporation of prior knowledge into neural networks to enhance model performance has evolved into a fundamental methodology, being widely used in real-world applications. The abstract prior knowledge, particularly the shape restrictions such as monotonicity, convexity, and homogeneity, hold critical importance within economic modeling frameworks, where functional specifications like production and utility functions inherently require certain shape restrictions to ensure both mathematical and theoretical validity in economic analyses. However, current methods exhibit fundamental limitations in effectively embedding abstract knowledge, since those methods either implement loss function modifications that fail to guarantee the desired shape, or require specifically designed structures through restricting the number or type of layers to certain forms--a parametric imposition that essentially limits model expressiveness. Our shape-restricted approximator is shown to be consistent and asymptotically normal under mild regularity conditions. Based on case studies, the proposed method outperforms the existing shape-restricted networks. Two-team zero-sum games model a broad range of real-world competitive interactions more effectively than traditional two-player games, yet they remain relatively underexplored. Existing successful approaches for two-player games, including Go, poker, and Stratego, are difficult to extend directly to team settings, as they cannot simultaneously account for inter-team competition and intra-team cooperation. In chapter 3, we investigate two-team zero-sum games under the setting where teammates cannot communicate, with the goal of approximating the team Nash equilibrium. Unlike existing methods such as TMECor \citep{TMECor_Team-PSRO} and GFXP \citep{FXP_team_comp}, which rely on communication or correlation devices by treating each team as a centralized joint player, our method directly tackles decentralized team settings without such coordination mechanisms. We propose a novel extension of Policy-Space Response Oracles (PSRO) \citep{PSRO} that incorporates multi-agent reinforcement learning (MARL) to compute joint cooperative best responses during population expansion. By leveraging shared team rewards, the proposed method captures cooperation within teams while preserving competitive optimization across teams. The proposed algorithm demonstrates a stronger tendency to converge to global Nash equilibria compared with classical PSRO based on some experimental results.
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    Development and Evaluation of Assistive AI Systems for Assessing News Trustworthiness
    (University of Waterloo, 2026-08-04) Zhang, Dake
    Online news shapes how people form opinions on topics such as science, health, and politics, yet the same environment that makes it widely accessible also enables low-quality and deceptive content to propagate at scale. This thesis studies how AI systems can help readers assess the trustworthiness of online news. Rather than predicting whether an article is true or false, we treat trustworthiness assessment as a process to be supported: a useful system should help readers ask the investigative questions that a careful reader would ask, and synthesize the external evidence and context needed to answer them. This framing is grounded in lateral reading, the strategy professional fact-checkers use when evaluating online information by searching beyond the page itself. We pursue system development and evaluation together because the two are inseparable in a new research area. This research program began with ReadProbe, a proof-of-concept retrieval-augmented LLM system that generated investigative questions, retrieved web evidence, and produced attributed answers. A subsequent pilot study, originally intended as a formative step toward a larger human-baseline study of question generation, characterized the kinds of questions university-affiliated readers wrote before and after brief lateral-reading guidance, and revealed useful design lessons. These lessons motivated the TREC 2024 Lateral Reading Track, the first shared benchmark in this area, which formalized question generation and document retrieval as foundational tasks. Results showed that question generation remains challenging even for frontier LLMs, while document retrieval is comparatively mature. A follow-up analysis further revealed that LLM-generated question lists are less diverse than those written by human experts and overlap little with them, suggesting that alignment with expert investigative priorities is the core open problem. These observations led to the TREC 2025 DRAGUN Track, which shifted the benchmark toward a more reader-oriented setting by introducing report generation as the main task and replacing direct grading with expert-authored, importance-weighted rubrics built through open-web research. To support the track, we developed an iterative multi-agent RAG system that simulates a lateral reader by interleaving query generation, multi-stage segment retrieval, information sufficiency evaluation, question generation, and report writing, which served as a strong starter-kit baseline. Finally, to make rubric-based evaluation reusable beyond the originally judged runs, we released an LLM-based AutoJudge system that mirrors the human assessment protocol and preserves run-level rankings well against the official human judgments. Through this research journey, three key findings stand out. First, generating investigative questions that align with expert priorities remains hard: even the strongest system in the DRAGUN track covered only about one-third of the importance-weighted rubric question space on average. Second, given a useful question, document retrieval is no longer the central bottleneck. The harder problems now sit at the planning and synthesis ends of the pipeline. Third, evaluation matters as much as system design, and rubric-based evaluation grounded in expert open-web research is a more useful and interpretable target than direct grading of system outputs. Overall, this thesis contributes a reader-centered framework for assistive AI in news trustworthiness assessment, along with shared tasks, datasets, systems, and a reusable evaluator that together support continued progress toward AI tools that help readers think more carefully about online news rather than think for them.
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    Evolving Trade-offs Towards Deployable Private Systems for Data Science
    (University of Waterloo, 2026-08-04) Humphries, Thomas
    There is no one-size-fits-all solution to preserving privacy in data science. While insights derived from sensitive data can benefit society, privacy is typically at odds with utility, performance, usability, or some combination of these objectives. Furthermore, each system differs in its definition of these objectives and the way they interact with one another. If the compromise required for any single objective is too great, the system will not be deployed, or worse, will be deployed with a weakened privacy guarantee, exposing users to potential harm. In this work, we address this challenge from multiple angles. First, through strategic algorithm design, our work creates private systems with improved trade-offs, enabling their deployment. This includes a more efficient protocol for the secure inference of deep machine learning models, a novel construction for aggregating key-value data in the local trust model, an evolutionary approach to improve the utility of private clustering, and a user-friendly interpretation of the error of private median queries. Second, we audit private systems to show the privacy risks associated with misleading privacy claims. In particular, through a privacy audit of machine learning, we highlight a difference between expectation and reality in privacy protections.
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    From Vulnerability to Viability in Small-Scale Fisheries: A Thematic Synthesis on Justice, Knowledge, and the Making of Transition
    (University of Waterloo, 2026-08-04) Shetu, Shahriyer Hossain
    Small-scale fisheries (SSF) sustain the livelihoods, food security, and cultural identities of approximately 500 million people globally, yet they face profound and compounding vulnerabilities arising from climate change, colonial dispossession, institutional exclusion, and the displacement pressures of an expanding blue economy. Despite their global significance, no systematic synthesis of the knowledge produced through the V2V Global Partnership Thematic Webinar Series has previously been conducted, leaving five years of transdisciplinary expert discourse inaccessible as a coherent scholarly contribution. This qualitative study addresses that gap through a reflexive thematic analysis of all 59 V2V thematic webinar sessions spanning January 2021 to December 2025, conducted within a constructivist paradigm and guided by an original tri-layered conceptual framework called the V2V Transition Ecosystem, which integrates the V2V vulnerability-to-viability core axis, the blue justice evaluative lens, and social-ecological systems theory. The analysis identified 74 codes across three research questions, organized into Core, Emerging, and New classifications and tracked temporally across five years. Five key findings emerge: the V2V webinar series documents a paradigmatic shift from vulnerability management to justice assertion; the five vulnerability types present across all five years are structural features of the global political economy of fisheries rather than developmental deficits; the eight Core transitional pathways are all fundamentally social, political, and relational rather than technical, and successful transitions consistently require multiple reinforcing pathways operating simultaneously; most theoretically consequential, the integrated analysis confirms a justice-viability nexus in which justice and viability are mutually constitutive, with every major vulnerability corresponding to a specific justice failure and every major pathway operating through a specific justice mechanism; and the rate of new thematic content declined sharply by 2025, suggesting that within the five-year period studied, the series had converged on a stable core conceptual framework for understanding SSF transitions, a pattern this thesis interprets as a feature of the period analyzed rather than a claim about the long-term trajectory of an ongoing knowledge platform. This thesis advances three principal theoretical contributions: the V2V Transition Ecosystem, an original tri-layered framework integrating the vulnerability-to-viability axis, the blue justice lens, and social-ecological systems theory; an original typology of five SSF vulnerability-viability transition contexts mapping specific vulnerability configurations to effective pathway combinations; and the justice-viability nexus, demonstrating that justice and viability are mutually constitutive rather than separate objectives - with direct implications for SSF policymakers, development organizations, community organizations, and international governance bodies seeking to support just and durable SSF transitions from vulnerability to viability.