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处理效应异质性的正交积分条件矩检验AI 解读

We propose a nonparametric integrated conditional moment (ICM) test for treatment effect heterogeneity across subpopulations defined by a given covariate subvector. Under unconfoundedness, the null is recast as a conditional moment restriction based on a Neyman-orthogonal score, which reduces the first-order sensitivity of the empirical process to nuisance parameter estimation. The test statistics are constructed as continuous functionals of a marked empirical process. We…

利用外部数据的贝叶斯单臂与混合对照临床试验设计：修正与扩展AI 解读

Limited patient availability complicates sample size determination in pediatric clinical trials. Although Bayesian methods incorporating external data offer a solution, rigorously controlling the type I error rate remains difficult. Psioda and Ibrahim (2019) proposed a simulation-based framework as a practical solution. However, although their framework was designed to relax the type I error control, this relaxation fails when the external data exhibit a large treatment…

潜在解释变量具有多个非线性测量时的部分识别AI 解读

We study linear regression when the regressor is latent and observed only through multiple noisy measurements, each a smooth but possibly nonlinear function of the latent variable. The problem is acute in the measurement of occupational exposure to artificial intelligence, where competing scores yield downstream estimates that differ by a factor of eleven. A regression on any single measurement recovers a source-specific coefficient rather than the structural one. We fix the…

从图到动态：在不确定性下探索因果回路图的干预点AI 解读

Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems. However, as qualitative and static representations, CLDs are limited in their ability to support dynamic analysis and inform intervention strategies. We propose Diagrams-to-Dynamics (D2D), a method for converting CLDs into exploratory system dynamics models in the absence of empirical data. With minimal user…

混合总体中的因果发现AI 解读

Causal discovery aims to learn causal structures up to certain symmetries. Diverse populations or changing environments give rise to heterogeneous data in the following sense: each population/environment is a ``source'' which idiosyncratically determines the forms of causal effects. From this perspective, the source is a latent common cause for every observed variable. While some methods for causal discovery can work around latent confounding in special cases, a global…

马尔可夫交替实验中经校准的期限加权局部投影设计AI 解读

We study temporal assignment design for Markov switchback experiments when the reported object is a dynamic local-projection target. We develop a calibrated selector that chooses the feasible persistence minimizing the covariance, HAC, residual-bootstrap, or realized-schedule risk of the estimator and reporting object specified before the experiment. A balanced homoskedastic Markov benchmark yields a closed form because the lagged-assignment information matrix is…

存在未知混杂因素时的二元关系处理效应估计AI 解读

This paper proposes estimation and inference methods for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a…

disco：分布合成控制AI 解读

The method of synthetic controls is widely used for evaluating causal effects of policy changes in settings with observational data. Often, researchers aim to estimate the causal impact of policy interventions on a treated unit at an aggregate level while also possessing data at a finer granularity. In this article, we introduce the new disco command, which implements the Distributional Synthetic Controls method introduced in Gunsilius (2023, Econometrica 91: 1105-1117).…

具有飞轮效应的分享诱导参与度因果估计AI 解读

Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-designed sharing feature can trigger a self-reinforcing ``flywheel effect'': reactivated users become potential sharers whose engagement propagates through the network over multiple rounds, amplifying total engagement. Measuring the causal impact of such sharing features is challenging, as their…

筛选何时误导：用于可靠因果发现的稳健孟德尔随机化检验AI 解读

Mendelian Randomization (MR) has been widely used as a standard approach for identifying causal effects in biomedical research. When the true exposure and outcome variables are unknown among many potential candidates, a common but problematic practice is to first screen for associations and then evaluate causal effects only among exposure-outcome pairs that show significant correlations. We demonstrate that the classical MR ratio estimator suffers from severe type I error…

复杂处理空间随机实验的耦合设计AI 解读

We describe a new family of experimental designs that extends the principle of stratified randomization to settings with continuous, constrained multivariate, and other irregular treatment spaces. Our approach is to first match units into homogeneous groups, then use Monte Carlo couplings to assign within-group treatments to be highly dispersed over the treatment space. We show that ensuring similar units receive dissimilar treatments improves estimation efficiency. The…

中介存在未观测混杂的前门因果结构：近端识别与估计AI 解读

Unobserved confounding is a fundamental obstacle in causal inference problems. In the graphical modeling literature, a general theory has been developed that allows identification in the presence of hidden variables, with some limitations. In particular, Pearl's celebrated front-door criterion allows nonparametric identification in the presence of unobserved common causes of the treatment and the outcome, however it requires the presence of an unconfounded variable that…

采用局部—全局收缩的非正态变换有向无环图半参数贝叶斯结构学习AI 解读

Bayesian structure learning of directed acyclic graphs (DAGs) is central to high-dimensional causal discovery, yet existing methods mostly assume multivariate Gaussian data, which is routinely violated by measurements displaying heavy tails, skewness, or bounded support. We address this by introducing NPN-DAG-HS, a fully Bayesian semiparametric DAG model coupling the nonparanormal family with horseshoe shrinkage on Cholesky off-diagonals via an extended-rank likelihood. This…

通过 Riesz 表示构建现代因果面板估计量的贝叶斯稳健性值AI 解读

We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial…

通过拉普拉斯近似实现可扩展的贝叶斯有向无环图结构学习及乳腺癌应用AI 解读

Structure learning of directed acyclic graphs (DAGs) from observational data is a foundational task in causal discovery and is widely used to infer regulatory networks from medical and genomic measurements. The Bayesian formulation quantifies model uncertainty and admits prior biological knowledge, but its practical use has been hampered by the super-exponential growth of the DAG space and by the intractability of the node-marginal likelihood under flexible, non-conjugate…

有向无环图上的深度高斯过程AI 解读

Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-regulatory networks, to transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and…

媒体效果衡量与“助攻乌龙”：归因、营销组合模型及个体增量效应AI 解读

We use the assisted own goal hypothesis as a lens into media measurement. A demand-generating (upper-funnel) advertising platform such as a short-video social network can cause an incremental purchase, yet see that purchase booked on -- and credited to -- a downstream trusted marketplace, because consumers who discover a product on the platform complete the transaction elsewhere, for example because of distrust of the generating platform as a psychological mechanism. Under…

混合解释变量下的误设回归：稳健推断与因果解释AI 解读

For analytic convenience, existing statistical frameworks either assume random or fixed regressors. However, it is a little awkward that they do not cover the practical case of estimating the average treatment effect in experiments with randomized treatments and non-randomized, fixed pretreatment covariates. We unify the literature by providing the theory for regressions with mixed regressors that contain both random and fixed components. Importantly, our theory allows for…

斯大林是否必要？苏联经济增长的反事实证据AI 解读

This article evaluates whether the exceptionally rapid growth of the Russian and Soviet economy after 1928 requires a specifically Stalinist explanation. The analysis treats this historical question as a sequence of increasingly demanding counterfactual comparisons rather than a binary comparison between observed growth and a single alternative. Long run output series from the Global Macro Database and the Maddison Project are combined with synthetic control, augmented…

网络元分析的因果视角AI 解读

Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches typically aggregate study-level treatment effects to obtain an overall estimate. We argue that the causal estimand should come first, with the aggregation derived only afterwards: the target population and the relevant sources of between-study heterogeneity should be explicitly defined before…

层级因果模型AI 解读

Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patients, or cities in states. In these settings, unit-level variables (e.g., a school's budget) may affect subunit-level outcomes (e.g., student test scores), and subunit-level characteristics may aggregate to influence unit-level outcomes. In this paper, we show how to analyze hierarchical data for causal inference. We introduce…

以双重校准借用观察数据提升随机试验 CATE 估计精度AI 解读

Understanding how treatment effects vary across patient characteristics is essential for personalized medicine, yet randomized controlled trials (RCTs) are often underpowered to detect heterogeneous treatment effects (HTEs). We propose a framework that improves the efficiency of conditional average treatment effect (CATE) estimation in RCTs by leveraging large observational studies (OS) while preserving RCT unbiasedness. Framing CATE estimation as a supervised learning…

区间识别集参数推断的功效性质AI 解读

This paper studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume the researcher has bounds estimators needed to construct the CIs proposed by Imbens and Manski (2004), Stoye (2009), and Stoye (2020), denoted by CI_alpha^1, CI_alpha^2, CI_alpha^3, and CI_alpha^4. We also assume these bounds estimators are ``ordered'': the lower bound estimator is less than or equal to the upper…

平稳随机动力系统中因果效应符号的可识别性AI 解读

We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure. Unlike existing approaches, we relax the assumption of a known diffusion matrix, thereby respecting the model's intrinsic scale invariance. Therefore, rather than recovering drift coefficients themselves, we introduce edge-sign identifiability: for a given causal structure, we ask whether the sign of a given drift entry is uniquely determined across…