每日简报

自删失模型下信息性缺失的纵向因果推断AI 解读

Longitudinal observational data are often subject to informative missingness. For example, in healthcare research using electronic health records, covariates and outcomes are measured only when patients interact with the healthcare system. Whether these data are observed usually depends on the unobserved variable values themselves, introducing a common form of data missing not at random. We develop identification and estimation methods for the effects of longitudinal…

超越来源域整体迁移：高维分位数回归的样本级学习AI 解读

This paper studies transfer learning for high-dimensional quantile regression with limited target samples and heterogeneous source domains. We propose a sample-level transfer learning method (SL-TL) that combines sample selection and importance weighting. Unlike existing source-level approaches that include or exclude entire source domains, SL-TL identifies transferable samples within each source domain and incorporates them through adaptive importance weights. The proposed…

弱信号下的政策学习AI 解读

Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We formalize this regime by modeling treatment-effect estimates from increasingly fine covariate partitions as Gaussian observations with bounded signal-to-noise ratios. We establish that, in general, the optimal treatment policy is not learnable in this setting. Even learning the optimal policy value suffers from impractically slow…

存在异质性时利用混合先验自适应结合随机试验与外部对照AI 解读

When resources are limited in a randomized trial, a common strategy is to augment data from the control arm with external controls. We propose a hybrid group sequential Bayesian design that uses this approach to compare time-to-event distributions. The design allows between-treatment effects to differ between patient subgroups, and adaptively combines subgroups that have similar hazard functions. The aim is to reduce the control arm sample size without compromising the…

借助知识迁移增强代表性不足子群体的工具变量分析AI 解读

Instrumental variable (IV) analysis in underrepresented target populations suffers from low statistical efficiency and weak IV bias due to limited sample size. Leveraging external knowledge from a source population with larger samples offers an appealing solution to this problem. We propose in this paper the KNowledge-powered IV Estimator for underrepresented Subpopulations (KNIVES), a framework that enhances the IV analysis for underrepresented subpopulations by…

以模型平均替代处理前趋势检验AI 解读

We study difference-in-differences (DID) estimation of average treatment effects on the treated when the researcher is uncertain about which pre-treatment periods satisfy the parallel trends assumption. Roth (2022) shows that pre-trend testing can induce bias, complementing the statistical literature on post-selection inference. We propose the model averaged difference-in-differences (MADID) estimator, a weighted average of the candidate $2\times2$ DID estimators. The…

DAG-CLIP：存在潜变量时的有向无环图学习框架AI 解读

Learning directed acyclic graphs (DAGs) to uncover causal mechanisms has attracted substantial attention in machine learning. While most existing methods focus exclusively on observed variables, many variables of substantive interest are latent constructs defined by statistical measurement models. Such constructs are particularly common in the social and behavioral sciences. In this paper, we propose a general statistical framework for DAG learning when some or all nodes of…

极端分位数回归加权最大似然估计量的存在性与一致性AI 解读

Estimating extreme conditional quantiles faces two major challenges: understanding complex nonlinear relationships between variables and accurately extrapolating into the tails of the distribution, where data are sparse. To address both issues, we introduce a novel approach that combines the theoretical framework of block maxima with the predictive power of generalized random forests. The conditional distribution of maxima is modeled by a generalized extreme value (GEV)…

边处理与节点结果的网络实验AI 解读

We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment…

计数数据的去偏机器学习：基于神经网络的部分线性 Poisson 模型AI 解读

We develop a novel debiased machine learning (DML) estimator to analyze count data, which are common in clinical or biomedical studies. Specifically, we estimate the effect of a binary treatment on count outcomes using a partially linear Poisson model. A key advantage of this model is that it represents the complex covariate structure through a nuisance function, which is estimated flexibly using neural networks. We use a Neyman-orthogonal score function to construct the…

在线实验的设计驱动推断：证据收集、检验与估计的联合最优性AI 解读

Online multifactorial experiments requires early stopping for practically negligible effects while controlling false stopping under continuous monitoring and retaining precise treatment-effect estimation. We develop a design-driven framework that chooses allocation and the evidence rule jointly, treating allocation as evidence collection and testing and estimation as complementary uses of the same evidence. For multifactor experiments with nuisance block effects and…

噪声即信号：相关抽样误差为代理指标选择提供排序信息AI 解读

North-star metrics such as customer lifetime value are often too slow and noisy to decide a short A/B test. Teams therefore rely on a proxy metric, commonly chosen by how closely its effects tracked the north star's across past experiments. Validating that choice, or any method for making it, is hard: the only benchmark is the noisy north star, and the number of available past experiments is limited. In addition, proxy and north-star effects are estimated on the same…

缺失的角点：尖锐因果界何时无法组合AI 解读

Sharp bounds on two potential-outcome means need not combine into sharp bounds on their difference. We characterize exactly when composition is guaranteed across environments sharing a joint potential-outcome law. For binary treatment and outcome, fix strict selection-probability bounds and interior treatment propensities. Subtracting the full model's sharp mean intervals gives sharp average-treatment-effect bounds for every compatible observed law if and only if the…

大规模需求估计的贝叶斯机器学习方法AI 解读

This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from…

通过因果数据融合衡量礼品卡项目的增量收入AI 解读

Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer…

Researchers often report an estimate and confidence interval only if a specification test does not reject - for example, only if a pre-trend test supports a difference-in-differences design. Does this make the reported confidence interval less reliable? We show that when the identifying assumptions hold, the answer is no: conditional on passing the pre-test, the confidence interval may over-cover but cannot under-cover. When the assumptions fail, pre-testing can reduce…

组合取胜：用于分布外预测的模型平均AI 解读

Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from mathematical psychology and data-driven approaches from machine learning. A key question arises as to how well these different models perform in forecasting, especially when considering trips of different characteristics from those used in estimation, i.e. out-of-distribution prediction, and whether better predictions can be…

We develop the first empirically implementable selective-inference framework for difference-in-differences with controls selected by pre-treatment fit. We construct confidence intervals for the average treatment effect on the treated (ATT), incorporating that selection and estimating the unknown full covariance from the same observations. Counterexamples establish undercoverage from Roth's fixed-comparison pretest adjustment and direct Gaussian-score application of…

双重高维线性混合模型下异质图模型的推断AI 解读

Inferring the structure of population-level functional connectivity networks from multi-subject functional magnetic resonance imaging data is essential for understanding the connectivity patterns underlying cognitive and behavioral functions. Graphical models provide a formal statistical framework for inferring the graph structure. However, the multi-level nature of functional magnetic resonance imaging data---where each level corresponds, for instance, to a…

近分离二元数据中惩罚GEE的有限样本方差估计AI 解读

Penalized generalized estimating equations (PGEE) stabilize point estimation for longitudinal binary data under near-separation, but inference still depends on how the sandwich variance is corrected. Existing corrections for PGEE can overadjust in high-leverage directions, require restrictive pooling assumptions, or add global regularization without explaining the bias. We establish first-order asymptotics for PGEE along convergent interior-root sequences and derive a matrix…

嵌套工具变量分析：转换者平均处理效应及可推广性AI 解读

Instrumental variables (IVs) are widely used to estimate causal effects from non-randomized data. A canonical example is a randomized trial with noncompliance, in which the randomized treatment assignment serves as an IV for the non-ignorable treatment received. Under a monotonicity assumption, a valid IV nonparametrically identifies the average treatment effect among a latent complier subgroup, whose generalizability is often under debate. In many studies, there exist…

Missingness in variables that define study eligibility criteria is a seldom addressed challenge in electronic health record (EHR)-based settings. Patients with incomplete eligibility information are typically excluded from analysis without consideration of the (implicit) assumptions being made, leaving study conclusions subject to potential selection bias. In an effort to ascertain eligibility for more patients, researchers may look back further in time prior to study…

Causal inference in continuous-time sequential decision problems is challenged by hidden confounding and partially observed states. We show that, under explicit structural assumptions, observability of the latent state enables identification of dynamic treatment effects through a continuous-time conditional front-door adjustment, even in the presence of hidden confounding. We derive a general adjustment formula and show that it reduces to a tractable state-space formula when…

预测处理效应分布：协变量调整方法AI 解读

Important questions for impact evaluation require knowledge not only of average effects, but of the distribution of treatment effects. The inability to observe individual counterfactuals makes answering these empirical questions challenging. I propose an inference approach for points of the distribution of treatment effects that uses predicted counterfactuals through covariate adjustment. I provide finite-sample valid inference using sample-splitting and asymptotically valid…