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整群随机试验的校准贝叶斯分析AI 解读

In cluster-randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approaches are standard practice for CRT analysis, Bayesian methods have emerged as a strong alternative. Previous work has investigated the use of Bayesian hierarchical models for continuous, binary, and count outcomes in CRTs, but these approaches focus on model-based treatment effect coefficients as the…

通过 Riesz 校准最优传输将随机试验效应迁移至现实人群AI 解读

Randomized trials support causal inference, but differences between trial and target populations can limit the transportability of treatment effects to real-world settings. Many existing approaches model the propensity of trial participation and can therefore be sensitive to model misspecification and weak overlap of the covariate distributions. Optimal Transport (OT) offers a different route by comparing the trial and target populations directly in covariate space. We…

通过留一变量法交叉验证因果发现AI 解读

We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the causal model. Specifically, given data on $X, Y, \boldsymbol{Z}=X, Y, Z_1,\dots,Z_k$, we apply the causal discovery algorithm separately to the 'leave-one-out' data sets $X, \boldsymbol{Z}$ and $Y, \boldsymbol{Z}$. We demonstrate that the two resulting causal models, in the form DAGs, ADMGs, CPDAGs…

计算预算约束下双重机器学习的均匀设计子抽样AI 解读

Double machine learning (DML) combines orthogonal scores with flexible nuisance estimation, but repeated cross-fitting and repeated analysis can make full-data workflows expensive. When a fixed computational budget requires a working sample of size $r\ll n$, simple random subsampling may cover the covariate space poorly and produce unstable treated--control composition. We propose Uniform Design Double Machine Learning (UD-DML), which maps a common low-discrepancy skeleton…

一般条件下极端事件因果效应的识别与推断AI 解读

Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behavior. This paper studies the identification of causal relations in extremes and derives resulting estimators and their asymptotic inference. As measure of causal dependence between extreme realizations of variables, we analyze the asymptotic behavior of the Causal Tail…

网络干扰下因果推断模型设定的随机化检验AI 解读

Analysis of experimental data becomes challenging when the underlying population is connected by a network. Exposure mapping is a common tool in the literature for defining and estimating spillover effects. These mappings reduce the dimensionality of the estimand, thereby facilitating identifiability. It is assumed that this mapping is correctly specified, leaving the choice of the exposure mapping to the analyst. This makes estimators of the spillover effect, such as the…

部分识别因果效应的模型无关协变量辅助推断AI 解读

Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper bounds; however, unless the covariates are discrete with relatively small support, this approach typically requires binning covariates or estimating the conditional distributions of potential outcomes given the covariates. Binning can result in substantial efficiency loss and…

用于观察研究因果推断的 CP 图AI 解读

Under the canonical setting of observational studies for causal inference, we derive a set of exact representations for pairwise differences among weighted average treatment effects as covariances between the conditional average treatment effect and the propensity score, up to positive scaling factors. These covariance representations bridge the two core concepts in causal inference with observational studies. They immediately imply that (i) the average treatment effect is…

观察研究敏感性模型下错误发现比例的同时推断AI 解读

We provide an approach to exploratory data analysis in observational studies with a single intervention and multiple endpoints. In such settings, the researcher would like to explore evidence for actual treatment effects while accounting for both false discoveries and the potential impact of unmeasured confounding. For any candidate subset of hypotheses about these outcomes, we provide sensitivity sets for the proportion of the hypotheses within the subset which are true.…

对函数形式稳健的空间自回归双重／去偏机器学习推断AI 解读

Spatial autoregressive inference is typically conditional on the spatial weights matrix, W, even though the underlying interaction structure is often unknown and empirical conclusions can be sensitive to its specification. This paper develops double/debiased machine learning inference for low-dimensional SAR parameters when the spatial interaction operator is learned flexibly from potentially endogenous characteristics. Within a maintained admissible support, interaction…

语言模型能否在不引入统计偏差的情况下提高随机实验功效？AI 解读

Randomized controlled trials (RCTs) are widely adopted for causal inference, yet cost and sample-size constraints limit power. We introduce CALM (Causal Analysis leveraging Language Models), a statistical framework that integrates insights generated by large language models (LLMs) into the analysis of RCTs using established causal estimators to increase precision while preserving statistical validity. In particular, CALM treats LLM-generated outputs as auxiliary prognostic…

存在中间事件时利用多状态数据估计路径处理效应AI 解读

During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur in addition to the primary event. The treatment can exert its effect on the primary endpoint along multiple pathways through intermediate events. Assumptions for identifying mediation effects, such as sequential ignorability in natural effects or the dismissible components condition in separable effects, fail because intermediate events act as treatment-induced…

模型质量对用户参与度的影响：版本内因果估计与合成数据验证AI 解读

Every team building Large Language Models (LLMs) faces a core challenge: offline benchmarks show performance gains and user engagement rises post deployment, but isolating cause from effect remains difficult. Simultaneous marketing, media coverage, and seasonal demand obscure whether model updates truly drive engagement gains. This paper presents a novel causal estimation approach that leverages non uniform quality improvements across capabilities within a single model…

网络干扰下分位数效应的一致推断AI 解读

This paper studies quantile treatment and spillover effects in network experiments. Average spillover effects reveal how treating a unit's neighbors affects its outcome on average, but mask the heterogeneity of these effects across the outcome distribution. We define structural quantile effects that compare outcome quantiles between exposure states, characterizing how own treatment and exposure to treated neighbors affect different parts of the outcome distribution. Building…

含噪网络测量下时变网络构成与预测依赖的分离AI 解读

A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice inserts a recorded network into an outcome regression and reads movements of the fitted coefficient as changes in transmission strength. When the network is latent, time varying, and measured with error, this reading fails: changes in strength and changes in composition can produce the same outcome…

结合负向控制的因果生存森林AI 解读

We study heterogeneous treatment-effect (HTE) estimation in observational survival studies commonly associated with both censored outcomes and unmeasured confounding. We integrate causal survival forests (CSF) with negative controls (NC) from proximal causal inference and introduce Negative Control Causal Survival Forests (NC-CSF), a flexible nonparametric HTE learner for survival analysis. Our approach uses a loss that incorporates proxy variables and Neyman…

识别分组局部平均处理效应与筛选有效工具变量AI 解读

Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local average treatment effects (LATE) despite relying on distinct instruments, even if several instruments violate the identifying assumptions. We use the fact that the LATE is homogeneous for instruments which (i) satisfy the LATE assumptions (instrument validity and treatment monotonicity in the…

分布双重差分：分位数反演前聚合还是反演后聚合？AI 解读

Staggered distributional difference-in-differences produces cohort-specific potential-outcome distributions, but applied work typically wants one overall quantile treatment effect. Two natural summaries---averaging cohort quantile treatment effects (QTTs) and mixing cohort distributions before inversion---use the same policy weights yet answer different target-population questions and can disagree even in sign. We derive the exact sharp interval for their gap conditional on…

多重处理因果推断中的重心融合 Gromov–Wasserstein 平衡AI 解读

Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimation variance, previous studies balance representation distributions between every pair of treatment patterns. However, such pairwise balancing scales quadratically with the number of treatment patterns and fails to preserve consistent local proximity structures across patterns, which degrades…

利用阳性组复检的群组检测评估处理效应AI 解读

Group testing is an established, highly cost-effective strategy for population-level disease surveillance that identifies positive individuals by pooling biological specimens. Originally introduced during World War II for large-scale screening and heavily utilized in modern high-throughput public health infrastructure, traditional group testing methods are restricted to purely associational analyses. Consequently, they lack the capacity to infer the direct causal effect of…

阈值筛选单臂前后研究的均值回归调整样本量设计AI 解读

Studies enrolling participants on the basis of an extreme baseline value are susceptible to regression to the mean (RTM), such that some observed pre-post change is expected even without treatment. Existing methods estimate and decompose RTM retrospectively. We extend this framework to prospective study design by deriving a closed-form sample-size method for continuous, cutoff-selected, single-arm pre-post studies. The method partitions anticipated total change into that…

异常值影响：依据统计后果进行检测AI 解读

We introduce a novel outlier detection method that utilizes a concrete measure of the statistical impact of an outlier rather than a vague notion of "unusualness". This allows practitioners to only flag points that materially affect the inferences made from the data while also ensuring these points are anomalous. This is of particular interest in experimentation, where outliers affect the validity of the causal inferences. In particular, we consider a potential outlier's…

半参数双重强化学习及其长期因果推断应用AI 解读

Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs), but fully nonparametric estimators can be unstable when intertemporal overlap is weak and occupancy ratios are high-dimensional. This limitation is especially relevant for long-term causal inference from randomized experiments: randomization ensures overlap in treatment assignment, but not over future state trajectories induced by…

利用 SIMEX 估计真实效应量分布AI 解读

Large-scale online experimentation produces noisy effect estimates, which can overstate gains and complicate decisions about launches and testing policies. We propose a nonparametric method based on SIMulation-EXtrapolation (SIMEX) to estimate the latent distribution of true effects from estimated average treatment effects with known variances. The method evaluates quantiles after adding progressively more simulated measurement noise and extrapolates the resulting inverse…