每日简报

多次划分下的训练与检验：分样本估计量的中心极限定理AI 解读

As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains valid inference on model properties by using separate subsamples to estimate the model and to evaluate it. However, this approach has two drawbacks, since each task uses only part of the data, and different splits can lead to widely different estimates. Averaging across multiple splits, I…

具有优化与统计保证的因果加性模型凸混合整数规划AI 解读

We study the problem of learning a directed acyclic graph from data generated according to an additive, non-linear structural equation model with Gaussian noise. We express each non-linear function through a basis expansion, and derive a maximum likelihood estimator with a group l0-regularization that penalizes the number of edges in the graph. The resulting estimator is formulated through a convex mixed-integer program, enabling the use of branch-and-bound methods to obtain…

高维线性回归估计：以 Post-Double-Autometrics 替代 Post-Double-LassoAI 解读

Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a…

跨实验与跨时间学习：应对 A/B 测试中的异质性AI 解读

A/B testing plays a central role in data-driven product development, guiding launch decisions for new features and designs. However, treatment effect estimates are often noisy due to short horizons, early stopping, and slowly accumulating long-tail metrics, making early conclusions unreliable. A natural remedy is to pool information across related experiments, but naive pooling potentially fails: within experiments, treatment effects may evolve over time, so mixing early and…

通过提示与检索增强生成流程构造投资组合风险压力情景AI 解读

We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses through a factor-based mapping that enables Value-at-Risk and Expected Shortfall assessment relative to classical…

适用于一般处理类型的双重差分非二分匹配框架AI 解读

Difference-in-differences (DID) is one of the most widely used causal inference frameworks in observational studies. However, most existing DID methods are designed for binary treatments and cannot be readily applied to non-binary treatment settings. Although recent work has begun to extend DID to non-binary (e.g., continuous) treatments, these approaches typically require strong additional assumptions, including parametric outcome models or the presence of idealized…

超越平行趋势：面板因果推断的识别策略稳健方法AI 解读

In this paper, we propose a new approach to causal inference with panel data. Instead of using panel data to adjust for differences in the distribution of unobserved heterogeneity between the treated and comparison groups, we instead use panel data to search for "close comparison groups" -- groups that are similar to the treated group in terms of pre-treatment outcomes. Then, we compare the outcomes of the treated group to the outcomes of these close comparison groups in…

Wasserstein 自回归的诊断检验AI 解读

Wasserstein autoregression provides a robust framework for modeling serial dependence among probability distributions, with wide-ranging applications in economics, finance, and climate science. In this paper, we develop portmanteau-type diagnostic tests for assessing the adequacy of Wasserstein autoregressive models. By defining autocorrelation functions for model errors and residuals in the Wasserstein space, we construct two related tests: one analogous to the classical…

因果推断的设计型理论AI 解读

Causal inference, as a major research area in statistics and data science, plays a central role across diverse fields such as medicine, economics, education, and the social sciences. Design-based causal inference begins with randomized experiments and emphasizes conducting statistical inference by leveraging the known randomization mechanism, thereby enabling identification and estimation of causal effects under weak model dependence. Grounded in the seminal works of Fisher…

计算流体动力学模拟中的初始暂态检测与收敛统计评估AI 解读

Time series data often contain initial transient periods before reaching a stable state, posing challenges in analysis and interpretation. In this paper, we propose a novel approach to detect and estimate the end of the initial transient in time series data. Our method leverages the reversal mean standard error (RMSE) as a metric for assessing the stability of the data. Additionally, we employ fractional filtering techniques to enhance the detection accuracy by filtering out…

工具变量模型中不可观测的异质溢出效应AI 解读

This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framework to settings with spillovers and establish sufficient conditions for their point identification without restricting…

本地收益大、净收益小：波多黎各首次行业进入的直接与溢出效应AI 解读

I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the municipio--industry level. Methodologically, I combine staggered-adoption difference-in-differences estimators that are…

反事实阈值与运行变量扭曲下的断点回归处理效应AI 解读

We develop a new framework for evaluating the total policy effect in regression discontinuity designs (RDD), incorporating both the direct effect of treatment on outcomes and the indirect effect arising from distortions in the running variable when treatment becomes available. Our identification strategy combines a conditional parallel trend assumption to recover untreated potential outcomes with a local invariance assumption that characterizes how the running variable…

面向现代数据的可训练中心性框架AI 解读

Measuring how central or typical a data point is underpins robust estimation, ranking, and outlier detection, but classical depth notions become expensive and unstable in high dimensions and are hard to extend beyond Euclidean data. We introduce Fused Unified centrality Score Estimation (FUSE), a neural centrality framework that operates on top of arbitrary representations. FUSE combines a global head, trained from pairwise distance-based comparisons to learn an anchor-free…

从市场结果估计电力供给曲线AI 解读

Researchers and electricity sector practitioners frequently require the supply curve of electricity markets and the price elasticity of supply for purposes such as price forecasting, policy analyses or market power assessment. It is common practice to construct supply curves from engineering data such as installed capacity and fuel prices. In this study, we propose a data-driven methodology to estimate the supply curve of electricity market empirically, i.e. from observed…

总体与条件因果效应估计的一般贝叶斯非参数方法AI 解读

We propose a Bayesian nonparametric (BNP) approach to causal inference using observational data consisting of outcome, treatment, and a set of confounders. The conditional distribution of the outcome given treatment and confounders is modeled flexibly using a dependent nonparametric mixture model, in which both the atoms and the weights vary with the confounders. The proposed BNP model is well suited for causal inference problems, as it does not rely on parametric…

超越 logit 的可转移效用匹配：一般异质性下的计算与估计AI 解读

We present a general framework for matching with transferable utility (TU) that accommodates arbitrary heterogeneity without relying on the logit structure. The optimal assignment problem is characterized by tractable linear programming formulation, allowing flexible error distributions and correlation patterns. We introduce an iterative algorithm that solves large-scale assignment problems with guaranteed convergence and an intuitive economic interpretation, and we show how…

时变混杂下错位处理采用的基于设计匹配框架AI 解读

Causal inference in longitudinal datasets has long been challenging due to dynamic treatment adoption and confounding by time-varying covariates. Prior work either fails to account for heterogeneity across treatment adoption cohorts and treatment timings or relies on modeling assumptions. In this paper, we develop a novel design-based framework for inference on group- and time-specific treatment effects in panel data with staggered treatment adoption. We establish…

使用 BRIDGE 与自适应 LASSO 的高维线性工具变量估计与推断AI 解读

This paper is an exposition of how BRIDGE and adaptive LASSO can be used in a two-stage least squares problem, to estimate the second-stage coefficients when the number of parameters p in both stages is growing with the sample size n. Facing a larger class of problems compared to the usual analysis in the literature, i.e., replacing the assumption of normal with sub-Gaussian errors, I prove that both methods ensure model selection consistency and oracle efficiency even when…

宏观与金融即时预测中的可解释机器学习：面向商业与政策决策的框架AI 解读

Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and robust uncertainty quantification. This article reviews recent developments in macroeconomic nowcasting and compares…

VARTA 模型的最大似然估计AI 解读

The literature on multivariate time series is, largely, limited to either models based on the multivariate Gaussian distribution or models specifically developed for a given application. In this paper we develop a general approach which is based on an underlying, unobserved, Gaussian Vector Autoregressive (VAR) model. Using a transformation, we can capture the time dynamics as well as the distributional properties of a multivariate time series. The model is called the Vector…

ARCADIA：使用智能体 AI 开展企业破产分析的可扩展因果发现AI 解读

This paper introduces ARCADIA, an agentic AI framework for causal discovery that integrates large-language-model reasoning with statistical diagnostics to construct valid, temporally coherent causal structures. Unlike traditional algorithms, ARCADIA iteratively refines candidate DAGs through constraint-guided prompting and causal-validity feedback, leading to stable and interpretable models for real-world high-stakes domains. Experiments on corporate bankruptcy data show…

基础模型先验AI 解读

Foundation models, and in particular large language models, can generate highly informative responses, prompting growing interest in using these ''synthetic'' outputs as data in empirical research and decision-making. This paper introduces the idea of a foundation prior, which shows that model-generated outputs are not as real observations, but draws from the foundation prior induced prior predictive distribution. As such synthetic data reflects both the model's learned…

高维二元时间序列的统计推断AI 解读

The analysis of non-real-valued data, such as binary time series, has attracted great interest in recent years. This manuscript proposes a post-selection estimator for estimating the coefficient matrices of a high-dimensional generalized binary vector autoregressive process and establishes a Gaussian approximation theorem for the proposed estimator. Furthermore, it introduces a second-order wild bootstrap algorithm to enable statistical inference on the coefficient matrices.…