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何时及如何开展先导实验：两阶段实验的设计规则AI 解读

Experimenters often run pilots, but how much a small pilot should shape the main-wave design has no settled answer. This paper shows how noisy pilot evidence should guide treatment assignment probabilities in two-wave experiments. Two canonical rules mark the extremes. Balanced assignment guards against worst cases but ignores evidence that one arm is noisier. Feasible Neyman allocation adapts, but with a finite pilot it can overreact to noise, producing arbitrarily large…

非恒定效应因果加性模型中的工具变量集合有效性检验AI 解读

Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validity is difficult to determine from observational data. This paper studies the problem of testing the validity of IV sets under Causal Additive Models with Non-Constant Effects (CAM-NCE). To address this problem, we propose a testable condition, termed the Cross Auxiliary-based independence Test…

用于多结果因果推断的逆回归AI 解读

With multiple outcomes in empirical research, a common strategy is to define a composite outcome as a weighted average of the original outcomes. However, the choices of weights are often subjective and can be controversial. We propose an inverse regression strategy for causal inference with multiple outcomes. The key idea is to regress the treatment on the outcomes, which is the inverse of the standard regression of the outcomes on the treatment. Although this strategy is…

使用 R 包 bsvars 快速高效分析贝叶斯结构向量自回归AI 解读

The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast and efficient estimation of these multivariate dynamic structural models, possibly with many variables, complex identification strategies, and non-linear characteristics. The models can be identified using adjustable exclusion restrictions and…

矩条件模型的曲率校准准贝叶斯更新AI 解读

Moment restrictions provide a flexible basis for quasi-Bayesian inference when a full likelihood is unavailable, but the weighting matrix in a quadratic moment criterion determines both the relative importance of the moments and the information scale of posterior updating. We propose curvature-calibrated quasi-Bayesian updating, which uses the inverse of the covariance (or long-run covariance) of the moment conditions evaluated at a self-consistent quasi-posterior center.…

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…

诊断测试与人工智能医疗器械的因果评估：效应修饰与信息增强决策AI 解读

Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments. However, unlike treatments, their impact on health outcomes is generally indirect because measuring diagnostic information typically does not itself affect patient outcomes, which complicates evaluation of their effectiveness. In this work, we develop a causal inference approach for evaluating diagnostic tests by distinguishing…

协变量分布迁移下连续处理效应的因果推广AI 解读

Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population. We study a covariate-shift setting in which covariates, treatment, and outcome are observed in a labelled source sample, while only covariates are observed in the target sample. We develop a two-sample local polynomial regression framework based on pseudo-outcomes that use source…

删失选择模型的分布回归方法AI 解读

We develop a distribution regression model with a censored selection rule, offering a semi-parametric generalization of the Heckman selection model. Our approach applies to the entire distribution, extending beyond the mean or median, accommodates non-Gaussian error structures, and allows for heterogeneous effects of covariates on both the selection and outcome distributions. By employing a censored selection rule, our model can uncover richer selection patterns according to…

不要丢弃单例：独立流失下配对实验的有效推断AI 解读

Pairwise randomization can yield substantial efficiency gains in experiments. Yet methodological guidance cautions against pairwise randomization, especially in settings with attrition, partly because common practices for estimation (i.e., pair fixed effects) imply discarding data from incomplete pairs thus exacerbating data loss from attrition. This practice of dropping incomplete pairs reduces statistical power of tests as well as precision of estimates, in paired…

Lasso选择后Cox模型的模型化自助法推断AI 解读

Inference after variable selection in Cox regression is difficult because simple Wald-type intervals after selection can have poor finite-sample conditional coverage. We study a model-based bootstrap for inference after Cox-Lasso variable selection. The Cox-Lasso is fitted once to the original data to select a set of variables, after which an unpenalized Cox model is fitted using only those variables. Bootstrap samples are generated from a semiparametric plug-in Cox model…

存在溢出效应的实验与准实验：基于设计的检验和推断AI 解读

Economic policies rarely affect only their direct targets. To study these spillovers, researchers summarize who else was treated with a simple exposure measure, such as the share of treated neighbors within a radius. But for many settings, economic theory provides little guidance on choosing the functional form (e.g., ring) of that measure or its parameters (e.g., radius). We show that the data can inform both choices. Correctly specified exposure measures imply…

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…

质心参照马氏匹配：大规模观察研究的因果推断框架AI 解读

Matching for causal inference can be computationally expensive at scale and can silently change the target population when overlap is limited. We propose Centroid-Referenced Mahalanobis Matching (CRM), which replaces global pairwise search with stratified sampling in two reference coordinates: each unit's Mahalanobis distance from the treated centroid and its Fisher coordinate along the treated-control mean shift. All covariates enter through the treated covariance geometry;…

个体化治疗效应预测校准的非参数评估AI 解读

An important aspect of the performance of algorithms that predict individualized treatment effects (ITE) is moderate calibration, i.e., the average treatment effect among individuals with predicted treatment effect of z being equal to z. The assessment of moderate calibration is challenging on two fronts: counterfactual responses are unobserved, and quantifying the conditional response function for models that generate continuous predicted values requires regularization.…

中断时间序列的因果中介分析：稳定中介加权及车辆排放政策应用AI 解读

Population-level policies are introduced at a fixed time and evaluated from a single series of aggregate outcomes, and the interrupted time series design estimates the total shift in an outcome after the intervention. When the policy is expected to act through a measurable pathway, the total effect is less informative than its decomposition into direct and indirect effects. We formulate causal mediation for a single interrupted time series and study stabilized mediator…

后验漂移与潜在隐藏混杂下的试验证据迁移AI 解读

Randomized trials provide internally valid treatment-effect evidence, but trial participants may not represent the target population. In contrast, observational studies are often closer to the target population, but their treatment assignment may be affected by possible hidden confounding. We develop a robust posterior-drift framework for estimating the average treatment effect in an observational target population when exact conditional-effect transportability may fail. The…

VAR 预测与脉冲响应的误设稳健收缩及选择AI 解读

Vector autoregressions (VARs) are vulnerable to dynamic misspecification, forcing researchers to navigate complex bias-variance trade-offs when forecasting or estimating impulse response functions (IRFs). We derive novel, task-specific selection criteria -- PC for forecasting and IRFC for IRF estimation -- based on asymptotically unbiased estimates of frequentist risk under local dynamic misspecification. These criteria give empirical researchers a fully data-driven and…

识别变异集中时固定效应回归的精确推断AI 解读

In fixed-effect regressions with many groups, fixed effects can absorb most identifying variation, leaving a handful of observations to carry what remains. When variation is this concentrated, conventional $t$-tests can reject a true null more than half the time, and any fixed critical value is either invalid or so conservative it has essentially no power. This paper builds an exact test from the design alone. A \textit{nuisance-annihilating contrast} is a linear combination…

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…

adjustedCurves：在 R 中估计混杂调整后的生存曲线AI 解读

Kaplan-Meier curves stratified by treatment allocation are the most popular way to depict causal effects in studies with right-censored time-to-event endpoints. If the treatment is randomly assigned and the sample size of the study is adequate, this method produces unbiased estimates of the population-averaged counterfactual survival curves. However, in observational studies, this is no longer the case. Instead, specific methods that allow adjustment for confounding must be…

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…

时变调节效应的选择后推断AI 解读

Causal effect moderation investigates how the effect of interventions (or treatments) on outcome variables changes based on observed characteristics of individuals, known as potential effect moderators. With advances in data collection, datasets containing many observed features as potential moderators have become increasingly common. High-dimensional analyses often lack interpretability, with important moderators masked by noise, while low-dimensional, marginal analyses…

调查抽样与因果推断中的最优控制变量AI 解读

We propose a family of control variate estimators for variance reduction in design-based survey sampling and causal inference, with and without interference. In these settings, inverse probability weighting (IPW) estimators are widely used, but may have large variance when sampling, treatment, or exposure probabilities are small. Building on the observation that several common estimators, including the Hajek estimator, the normalized estimator, the augmented inverse…