Filtering without recursion and some of its uses in financial economics
本文提出了一种非递归滤波器，通过最小化折现的凸组合损失来处理时间序列，并应用于金融资产交易数据中以消除微观结构噪声对波动率估计的影响。
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AI worsens climate change, integrated assessment shows
研究提出一个整合AI对排放、产出和气候损害影响的框架，分析AI加剧气候变化的问题，区分ICT类和工业革命类AI前景，发现减缓措施与AI发展相辅相成。
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Real-time Program Evaluation using Anytime-valid Rank Tests
This paper addresses the limitation of traditional counterfactual mean estimation methods—such as difference-in-differences and synthetic control—in program evaluation under streaming data settings, where real-time causal inference is infeasible. We propose a sequential causal inference framework valid at any time point. Our key innovation is the first integration of exchangeability assumptions with sequential rank testing, enabling an anytime-valid hypothesis test that requires no prespecified sample size and supports both early stopping and delayed rejection. Theoretically, the method strictly controls Type-I error even under mild violations of exchangeability. Simulation results show a modest reduction in asymptotic statistical power but substantial gains in decision timeliness and adaptability. This framework provides a practical, real-time causal inference tool for dynamic policy evaluation.
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Gene-environment interplay and public policies
Existing gene–environment interaction (GxE) research lacks a policy-oriented theoretical framework and empirically appropriate methodologies, hindering the identification of heterogeneous policy effects across genetically distinct subpopulations. Method: This project develops the first policy-goal-driven GxE taxonomy, overcoming limitations of conventional interaction-term modeling. It integrates multilevel GxE modeling, polygenic index (PGI) quantile interaction analysis, and natural experiments in education policy to systematically map empirical GxE evidence for educational interventions. Contribution/Results: The study delivers an actionable methodological framework and empirical benchmarks for designing precision, equity-centered education policies. By explicitly linking genetic susceptibility with policy-relevant environmental variation, it substantially enhances the policy relevance, interpretability, and translational value of GxE research—bridging a critical gap between behavioral genetics and evidence-based policymaking.
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Filtering without recursion and some of its uses in financial economics
本文提出了一种非递归滤波器，通过最小化折现的凸组合损失来处理时间序列，并应用于金融资产交易数据中以消除微观结构噪声对波动率估计的影响。
0 citationsRead paper
AI worsens climate change, integrated assessment shows
研究提出一个整合AI对排放、产出和气候损害影响的框架，分析AI加剧气候变化的问题，区分ICT类和工业革命类AI前景，发现减缓措施与AI发展相辅相成。
0 citationsRead paper
Real-time Program Evaluation using Anytime-valid Rank Tests
This paper addresses the limitation of traditional counterfactual mean estimation methods—such as difference-in-differences and synthetic control—in program evaluation under streaming data settings, where real-time causal inference is infeasible. We propose a sequential causal inference framework valid at any time point. Our key innovation is the first integration of exchangeability assumptions with sequential rank testing, enabling an anytime-valid hypothesis test that requires no prespecified sample size and supports both early stopping and delayed rejection. Theoretically, the method strictly controls Type-I error even under mild violations of exchangeability. Simulation results show a modest reduction in asymptotic statistical power but substantial gains in decision timeliness and adaptability. This framework provides a practical, real-time causal inference tool for dynamic policy evaluation.
0 citationsRead paper
Gene-environment interplay and public policies
Existing gene–environment interaction (GxE) research lacks a policy-oriented theoretical framework and empirically appropriate methodologies, hindering the identification of heterogeneous policy effects across genetically distinct subpopulations. Method: This project develops the first policy-goal-driven GxE taxonomy, overcoming limitations of conventional interaction-term modeling. It integrates multilevel GxE modeling, polygenic index (PGI) quantile interaction analysis, and natural experiments in education policy to systematically map empirical GxE evidence for educational interventions. Contribution/Results: The study delivers an actionable methodological framework and empirical benchmarks for designing precision, equity-centered education policies. By explicitly linking genetic susceptibility with policy-relevant environmental variation, it substantially enhances the policy relevance, interpretability, and translational value of GxE research—bridging a critical gap between behavioral genetics and evidence-based policymaking.
0 citationsRead paper