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波动率校正预测区间：面向概率时间序列预测的局部自适应不确定性量化AI 解读

Prediction intervals from probabilistic time-series forecasters are commonly assessed by marginal coverage, the long-run proportion of observations falling inside the intervals. However, forecast errors in time series are often serially dependent and heteroskedastic, so prediction intervals can attain near-nominal marginal coverage while miscovering systematically during periods of larger local error variance. We identify and address this failure mode, which we call…

谱自监督学习是否为密度比估计的极小极大最优方法？AI 解读

Spectral self-supervised learning is a method of density ratio estimation for the setting in which only samples drawn from the joint density of the numerator are available. The method has extensively been studied in terms of the computational properties, such as optimal solutions and connections to representation learning. However, theoretical understanding of its statistical property is still insufficient. In this work, we study three questions, namely, the minimax…

应对因果分析中未检验假设的主观贝叶斯方法AI 解读

Causal analyses using observational data rely on identifying assumptions that cannot be verified from the observed data alone. When such unchecked assumptions fail, effect estimates may be biased and uncertainty may be understated. We propose the Adjusted Model for Unchecked Assumptions (AMUA), a subjective Bayesian framework that propagates uncertainty about the joint validity of key causal assumptions through a two-component posterior mixture. One component corresponds to…

利用中介变量更有效地迁移因果效应AI 解读

We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowledge of population heterogeneity or summary-level statistics to achieve improved efficiency. We first propose a one-step semiparametric estimator that assumes knowledge of the baseline covariates driving the heterogeneity across populations. This approach improves efficiency by transporting only…

交错采纳面板中的因果推断：正确的比较集取决于干预类型AI 解读

A powerful data structure for causal inference is staggered adoption: many units are observed over many time periods, during which some units adopt an irreversible treatment at various time points while others remain untreated. Popular methods that apply in this setting include difference in difference, fixed effects, matching, and synthetic control. All of these methods compare the future outcomes of treatment adopters and non-adopters to answer the question: for units who…

未知网络结构下短面板同伴效应的检验AI 解读

We propose an instrument-based test for peer effects in short-T panels when the underlying network is unknown. The test avoids estimating the network by aggregating pairwise comparisons between covariate-projected outcomes of potential peers and residualized own outcomes. The statistic is designed for fixed-T, large-N settings with individual fixed effects, heterogeneous slopes, heteroskedasticity, and serial dependence within units. We also develop a weighted extension that…

This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid…

We study identification and estimation of panel data models that are characterised by a common, potentially, infinite-dimensional parameter, $θ$ and an unknown transformation $g(α_{i},γ_{t})$ of two-way fixed effects $(α_{i},γ_{t})$ that are themselves unknown. We treat $g$ as a nonparametric object to be identified/estimated together with $θ$ and $(α_{i},γ_{t})$. We first provide high-level assumptions under which $θ$ together with the image of $g$, $\{g(α_{i},γ_{t})\}$, is…

正性约束下的贝叶斯图模型：可扩展的广义似然方法AI 解读

We develop a computationally scalable Bayesian framework for precision matrix estimation in Gaussian graphical models under total positivity constraints. To overcome the high computational cost of the Gaussian likelihood, we adopt a generalized Bayesian approach based on the D-trace loss, which eliminates the log-determinant term and enables efficient optimization while allowing relaxation of positive definiteness during sampling. Sparsity is induced via spike-and-slab…

通过样本分割评估 A/B 测试方法：理论与实践AI 解读

We develop a theoretical framework for sample splitting in A/B testing environments, where data for each test are partitioned into two splits to measure methodological performance when the true impacts of tests are unobserved. We show that sample-split estimators are generally biased for full-sample performance but consistently estimate sample-split analogues of it. We derive their asymptotic distributions, construct valid confidence intervals, and characterize the…

未观测目标成员资格下高危人群的组平均处理效应估计AI 解读

Estimating treatment effects within a prespecified high-risk population is challenging. When risk status is unobserved or the target population is small, analyses restricted to high-risk individuals can be unstable or inefficient. We propose a risk-score-weighted soft-targeting (RSW) estimator for group average treatment effects in high-risk populations. It accommodates two settings: when high-risk status is observed, the membership probability can be estimated within the…

LASSO估计误差的精确收敛速率：特征空间分解方法AI 解读

In this paper, we use the Feature Space Decomposition (FSD) method to establish an explicit, non-asymptotic, instance-optimal convergence rate for the estimation error of the LASSO in the Gaussian linear regression problem for any signal, any diagonal covariance and any regularization parameter above $σ_ξ/\sqrt{N}$ where $σ_ξ$ is the noise variance and $N$ the number of data.

平均处理效应的重匹配估计量AI 解读

Matching estimators are widely applied in practice for their great intuitive appeal. However, simple matching estimators with a fixed number of matches ($M_0$) are generally inefficient. In this article, we propose matching estimators with a variable number of matches to gain efficiency via rematching. Rather than increasing $M_0$ to gain precision, which introduces an increase in bias, the key is to rematch the treated units from the opposite direction to utilize unmatched…

We study identification of a structural group effect when the group indicator $G\in\{0,1\}$ is unobserved but the analyst observes a calibrated probability score $p$ satisfying $E[G\mid p,X]=p$. Under a constant-coefficient structural mean model, the latent-group coefficient $τ$ is point-identified by a closed-form ratio of observable moments whose denominator is the residual score variance $V^{*}=E[(p-E[p\mid X])^2]$. Identification fails exactly when the score is a…

巴西主权压力下的货币政策预测误差与通胀补偿AI 解读

This paper examines whether inflation compensation around Brazilian monetary policy decisions is associated differently with policy forecast errors when sovereign spreads are relatively elevated. Public Tesouro Direto quotations, meeting-specific Focus forecasts and a lagged EMBI indicator yield 68 three-year and 60 five-year event observations. The outcome is a matched-horizon retail yield proxy; the policy variable is the announced Selic target minus a preceding-week…

Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with the performance of the resulting causal estimator is not well understood. We studied this question in a partially linear model using Monte Carlo simulations, comparing ordinary least squares (OLS), generalized additive models (GAMs), XGBoost, and Double Machine Learning with XGBoost (DML-XGBoost) with independent and with clustered observations. We…

Randomized controlled trials (RCTs) are the gold standard for evaluating causal effects but are often costly and difficult to scale; consequently, they are frequently augmented with auxiliary external controls in many applications. Prior approaches to borrowing such data typically rely on exchangeability, under which the external controls are readily usable for inference in the trial population. In practice, however, differences in eligibility criteria, standard of care, and…

I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent…

Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying…

For subvector inference in the linear instrumental variables model under homoskedasticity but allowing for weak instruments, Guggenberger, Kleibergen and Mavroeidis (2019) (GKM) propose a conditional subvector Anderson and Rubin (1949) (AR) test that uses data-dependent critical values that adapt to the strength of the parameters not under test. This test has correct size and strictly higher power than the test that uses standard asymptotic chi-square critical values. The…

CAQ：面向在线实验的聚类渐近任意时点有效分位数处理效应AI 解读

Confidence sequences let an experiment be monitored continuously without inflating error, but the existing constructions for quantiles assume that observations arrive independently. In online experiments this rarely holds: randomization assigns users, while the metric is computed over the many correlated events each user generates, so intervals built for independent data are too narrow and miss the truth more often than their nominal rate allows. We propose CAQ, a confidence…

We propose double machine learning (DML) estimators of the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in the multivariate sample selection model with multiple ordinal selection equations. We establish asymptotic normality of the proposed estimators and study finite sample properties on simulated data. Specifically, the results of the analysis suggest that without addressing multivariate sample selection, the estimates of the causal…

重建不足够：学习多模态表征下的双稳健反事实与干预中介分析AI 解读

Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the information that causal identification requires. We study counterfactual outcomes under alternative treatments, including phenotype-specific effects and patient-level counterfactual risk profiles of the kind digital twins provide, and interventional direct and indirect effects…

This paper studies the identification and estimation of a semiparametric dynamic logit model with nonparametric link formation, in which a time-varying index of unobserved characteristics affects outcomes and links. The outcome depends on covariates, the past outcome, a fixed effect, and an unknown function of that index. Conditioning on a switch by the same agent across waves removes the fixed effect but not the change in the unknown function. When this change is common…