每日简报

双边市场双重随机化实验的回归调整AI 解读

Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for estimating total, spillover, and direct effects in MRDs. We derive minimum asymptotic variance estimators among a broad class of linearly adjusted estimators, without assuming a linear model on the potential outcomes. Surprisingly, the optimal regression adjustments are estimable from data and are…

聚类数据下回归不连续设计的推断AI 解读

Clustered sampling is prevalent in empirical regression discontinuity (RD) designs, but it has not received much attention in the theoretical literature. In this paper, we introduce a general model-based framework for such settings and derive high-level conditions under which the standard local linear RD estimator is asymptotically normal. We verify that our high-level assumptions hold across a wide range of empirical designs, including settings of growing cluster sizes. We…

合成控制的常见误解与实践建议AI 解读

To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on units over time, synthetic control (SC) methods provide an opportunity to construct a valid comparison by…

线性混合因果表征的多域经验贝叶斯学习AI 解读

Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiability has been extensively studied for CRL, estimation has been less explored. In this paper, we explore the use of empirical Bayes (EB) to estimate causal representations. In particular, we consider the problem of learning from data from multiple domains, where differences between domains are modeled by interventions in a shared…

结合 T-learning 与 DR-learning：因果对比的 oracle 有效估计框架AI 解读

We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks: (i) their practical applicability can be hindered by non-convex loss functions; and (ii) they…

通过时间关联、观察与实验数据识别长期处理效应AI 解读

Recent literature proposes combining short-term experimental and long-term observational data to provide alternatives to conventional observational studies for the identification of long-term average treatment effects (LTEs). This paper re-examines the identification problem and uncovers that assumptions restricting temporal link functions -- relationships between short-term and mean long-term potential outcomes -- are central in this context. The experimental data serve to…

无度量结构网络数据的极限定理AI 解读

This paper develops limit theorems for random variables with network dependence, without requiring the individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit theorems in network statistics and econometrics, which are based on weak dependence concepts such as strong mixing, near-epoch dependence, or $ψ$-dependence. All these weak dependence concepts presuppose an underlying metric. By relaxing the…

电子健康记录研究中处理未测混杂的代理变量方法AI 解读

Electronic health records (EHR) are widely used to study clinical decisions, yet unmeasured confounding remains a persistent challenge. Proxy variables offer a potential solution. In EHR data, clinicians already record many such measurements (e.g., vitals), each revealing something about a patient's underlying health. Despite this, proxy-based methods are rarely used in practice. We introduce a new way to use proxies to adjust for unmeasured confounding. Our approach uses a…

高效且易用的离散选择实验：R 包 DCEtoolAI 解读

Discrete Choice Experiments (DCEs) are widely used to elicit preferences for products or services by analyzing choices among alternatives described by their attributes. The quality of the insights obtained from a DCE heavily depends on the properties of its experimental design. While early DCEs often relied on linear criteria such as orthogonality, these approaches were later found to be inappropriate for discrete choice models, which are inherently non-linear. As a result,…

应调整哪些协变量？观察研究中对模型设定稳健的因果推断AI 解读

In observational causal inference, domain knowledge often leaves multiple covariate adjustments plausible, yet which sets satisfy ignorability is untestable. Different adjustment sets can yield conflicting estimates of the average treatment effect, and standard remedies (adjusting for their union or intersection, or reporting the union or convex hull of confidence intervals) can fail or produce intervals whose width does not vanish with sample size. We propose a…