每日简报

见树亦见林：BART 的高斯过程极限AI 解读

Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous theoretical work has attempted to explain BART's superior performance by establishing posterior contraction rates for standard BART models, but these rates depend strongly on the number of covariates. Here, we take a different approach and study the behavior of BART as the number of trees grows towards infinity. We show that in this…

从控制到处理：超越已观测面板的因果预测AI 解读

Conventional panel data methods recover retrospective counterfactuals within the observed horizon. Many policy decisions, however, are prospective: how would an untreated unit evolve beyond the observed panel under an intervention it has not previously experienced? We develop a causal forecasting framework that combines the counterfactual logic of synthetic controls with multivariate time-series extrapolation. Under a latent factor model, we impose low-rank structure on the…

意大利北部PM10评估的三种分布方法AI 解读

We propose three spatial methods for estimating the full probability distribution of PM10 concentrations, with the ultimate goal of assessing air quality in Northern Italy. Moving beyond spatial averages and simple indicators, we adopt a distributional perspective to capture the complex variability of pollutant concentrations across space. The first proposed approach predicts class-based compositions via Fixed Rank Kriging; the second estimates multiple, non-crossing…

随机拥堵下的实验设计与估计AI 解读

We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply or excess demand. Such congestion gives rise to cross-unit interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. In current practice, one of the most widely used ways to address stochastic congestion is to use switchback experiments that alternatively turn a target intervention on and…

基于核岭回归的条件平均处理效应迁移学习AI 解读

The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i) the source and target populations, as well as (ii) the treatment and control groups within the source. We propose a novel method for overlap-adaptive…

连接预测与归因：利用同化因果推断识别前向与后向因果影响范围AI 解读

Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with dynamical models. It utilizes Bayesian data assimilation to trace causes back from observed effects by quantifying the reduction in uncertainty. ACI advances the detection of instantaneous causal relationships and the intermittent reversal…

波动率推断的稳健化高斯拟似然方法AI 解读

We consider statistical inference for a class of continuous semimartingale regression models based on high-frequency observations subject to contamination by finite-activity jumps and spike noise. By employing density-power weighting and Hölder-inequality-based normalization, we propose easy-to-implement, robustified versions of the conventional Gaussian quasi-maximum-likelihood estimator that require only a single tuning parameter. We prove their asymptotic mixed normality…

Hurst 指数小于四分之三时混合分数布朗运动的局部渐近正态性AI 解读

This paper establishes the Local Asymptotic Normality (LAN) property for the mixed fractional Brownian motion under high-frequency observations with Hurst index $H \in (0, 3/4)$. The simultaneous estimation of the volatility and the Hurst index encounters a degeneracy problem in the Fisher information matrix.

稀疏双边互动模型中的互补性AI 解读

This paper studies complementarities in two-sided interaction models when only a sparse subset of potential matches is realized. I introduce the Tukey model, adding one complementarity parameter to two-way fixed effects. Variation within a four-cycle in the observed matching network identifies this parameter, while connectedness permits identification of agent productivities. I propose a cycle-based estimator that avoids estimating latent productivities. The estimator is…

观察数据因果推断中的最优传输入门AI 解读

The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science. The fundamental idea of analyzing probabilities by comparing their underlying state space naturally aligns with the core idea of causal inference, where understanding and quantifying counterfactual states is paramount. Despite this intuitive connection, explicit research at the intersection…

干扰参数满足不等式时的简单自适应置信区间AI 解读

Inequalities may appear in many models. They can be as simple as assuming a parameter is nonnegative, possibly a regression coefficient or a treatment effect. This paper focuses on models with one inequality and proposes an inequality-imposed confidence interval (IICI) that has particularly attractive features. The IICI is simple in that it does not require simulations or tuning parameters. Also, the IICI is adaptive to the slackness of the inequality, uniformly valid, and…

时间序列中基于最小二乘的置换检验AI 解读

This paper studies permutation tests for regression parameters in a time series setting, where the time series is assumed stationary but may exhibit an arbitrary (but weak) dependence structure. In such a setting, it is perhaps surprising that permutation tests can offer any type of inference guarantees, since permuting of covariates can destroy their relationship with the response. Indeed, the fundamental assumption of exchangeability of errors required for the…

计数型中间变量的主分层与 BART：生育对女性就业的效应AI 解读

Estimating the causal effect of fertility on women's employment is challenging because fertility and labour-market decisions are jointly determined. Instrumental-variable strategies are widely used, but credible instruments are rare and their validity often depends on covariates. Two-stage least squares, the dominant implementation, does not flexibly accommodate covariate-dependent instrument validity and is poorly suited to count-valued treatment and effect heterogeneity…

整群随机试验中胜负统计量的样本量设计AI 解读

Composite endpoints are increasingly used in clinical trials to capture treatment effects across multiple or hierarchically ordered outcomes. Although inference procedures based on win statistics, such as the win ratio, win odds, and win difference (net benefit) have gained traction in individually randomized trials, their methodological development for cluster-randomized trials remains limited. In particular, there is no formal framework for power and sample size…

一周价格翻倍：进口隔离市场中的农业价格波动预测AI 解读

Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up. This study develops a machine learning framework to forecast such volatility by incorporating supply-chain-aware features and explicitly modelling the country's two cultivation seasons, Maha (October-April) and Yala (May-September). An integrated dataset was constructed by combining retail and farmer-gate prices with origin-aligned…

高维矩阵值时间序列的时空自回归AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals across multiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

加权最小二乘估计量对遗漏变量的敏感性AI 解读

We introduce tools for assessing the sensitivity, to unobserved confounding, of a common estimator of causal effects that employs weights: the weighted linear regression of the outcome on the treatment and observed covariates. This estimator's bias is a function of two intuitive weighted partial $R^2$ values: (i) the proportion of weighted variance in the treatment that unobserved confounding explains given the covariates and (ii) the proportion of weighted variance in the…

学习该学什么：结合实验与观察证据的实验设计AI 解读

Experiments deliver credible treatment-effect estimates but, because they are costly, are often restricted to specific sites, small populations, or particular mechanisms. A common practice across several fields is therefore to combine experimental estimates with reduced-form or structural external (observational) evidence to answer broader policy questions, such as those involving general equilibrium effects or external validity. We develop a unified framework for the design…

森林核平衡权重：由结局引导的因果推断特征AI 解读

While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning important features to balance. In this paper, we propose forest kernel balancing, which leverages the underappreciated fact…

通过构造工具变量实现测量误差稳健的因果推断AI 解读

Measurement error can often be harmful when estimating causal effects. Two scenarios in which this is the case are in the estimation of (a) the average treatment effect when confounders are measured with error and (b) the natural indirect effect when the exposure and/or confounders are measured with error. Methods adjusting for measurement error typically require external data or knowledge about the measurement error distribution. Here, we propose methodology not requiring…

中介分析中复合零假设的最优检验AI 解读

The indirect effect of an exposure on an outcome through an intermediate variable can be identified by a product of two regression coefficients under certain causal and regression modeling assumptions. In this context, the null hypothesis of no indirect effect is a composite null hypothesis, as the null holds if either regression coefficient is zero. A consequence is that traditional hypothesis tests are severely underpowered near the origin (i.e., when both coefficients are…

显示性社会网络AI 解读

The linear-in-means model is the standard empirical model of peer effects and asks that an agent's choice or outcome is a combination of their ideal point and the mean outcome of their group. Using choice data and exogenous group variation, we develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a condition about differentiating the behavior of each agent in a consistent manner. We then…

利用非二元工具变量估计平均处理效应AI 解读

Non-binary instrumental variables, especially continuous ones, are common in practice. A binary recoding induces a Wald ratio but may discard useful variation and reduce efficiency. Although fully nonparametric approaches can in principle use the entire instrument, they often require high-dimensional nuisance estimation which can be unstable with rich covariates. We address this problem by developing a generalized Wald estimand for binary treatments that uses the full…

非对称 Huber 周期图AI 解读

This paper introduces a novel spectral M-estimator, called the asymmetric Huber periodogram (AHP), as a generalization of the ordinary periodogram (PG), the quantile periodogram (QP), and the Huber periodogram (HP). The AHP is constructed via trigonometric asymmetric Huber regression (AHR), in which a specially designed loss function replaces the squared $\ell_2$ loss used to define the PG. Relative to the QP, the AHP can be more computationally efficient, and relative to…