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动态处理方案的尖锐因果界AI 解读

We study dynamic treatment regimes under contemporaneous confounding: at each stage, an unmeasured factor may affect both treatment and the next observed state, but has no further direct effect on later stages. From the observational distribution, the causal graph, and specified structural restrictions on possible next states, we construct at each state--treatment pair the set of transition probabilities compatible with this information. These local sets require no…

结合 Student-t 经验贝叶斯与非局部先验的稳健稀疏群组动态因果建模AI 解读

Dynamic causal modeling (DCM) estimates directed effective connectivity, while parametric empirical Bayes (PEB) supports group inference using subject-specific posterior summaries. Standard PEB relies on Gaussian models and continuous shrinkage, making it sensitive to atypical estimates and unable to distinguish negligible from nonzero effects. We develop a robust and sparse group-DCM extension combining a Student-t likelihood with a spike-and-slab prior using a nonlocal…

基于泊松稀释结构方程模型的计数数据因果 DAG 识别AI 解读

Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited. The Poisson branching structural causal model (PB-SCM) provides a count-valued analogue of linear structural equation models using binomial thinning and independent Poisson exogenous variables, but its causal DAG is generally only partially identifiable. Building on this framework, we…

双重稳健泛函最优估计中的辅助函数调参与样本分割AI 解读

Estimators of doubly robust functionals typically rely on estimating two complex nuisance functions, such as the propensity score and conditional outcome mean for the average treatment effect functional. We consider the problem of how to estimate nuisance functions to obtain optimal rates of convergence for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. For several plug-in…

部分识别处理效应的广义信仰推断框架AI 解读

Over the past two decades, there has been renewed interest in fiducial inference, a statistical framework originally proposed by R. A. Fisher in the 1930s. Existing contributions, however, have largely focused on point-identified models, where the parameter vector is uniquely determined by the joint distribution of the observables and the model assumptions. This paper develops a unified fiducial inference procedure for partially identified models, thereby extending the…

结构未知模型下双重机器学习估计量的渐近不可容许性AI 解读

Structure-agnostic (SA) models introduced by Balakrishnan et al. (2026) aim to reflect the general lack of knowledge of structural assumptions on data-generating laws such as smoothness or sparsity in practice. Roughly speaking, SA models restrict the observed-data generating law to be in some rn-neighborhood of (black-box machine learning) estimates, treated as given and fixed, where rn encodes the convergence rates of the estimates to the truth. Under SA models,…

处理持续性如何影响纵向观察数据中的因果估计表现AI 解读

Longitudinal clinical data are increasingly available, offering opportunities to study treatment effects over time but also raising challenges related to time-varying confounding and evolving treatment decisions. We investigate how longitudinal treatment dynamics affect causal effect estimation when baseline and longitudinal methods target different causal estimands. Using a structural causal model (SCM), we simulate data with time-varying confounding, binary treatment, and…

部分干扰与增量倾向得分政策下的高效双重差分估计AI 解读

This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and spillover average treatment effects on the treated, establish their identification, and derive their efficient influence functions, from which we construct a cross-fitted estimator. Simulations evaluate its finite-sample performance. An application to China's New Rural Pension Scheme recovers the…

重尾分布下位置不变的极端分位数处理效应估计AI 解读

Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and their estimation at extreme quantile levels is of central interest in applications where the target quantiles lie far beyond the range of the data. For heavy-tailed potential outcomes, existing extremal QTE estimators rely on extrapolation combined with a causal extreme value index (EVI) estimator, but the resulting estimator is not invariant under a common location…

因果状态依赖局部投影AI 解读

State-dependent local projections (LPs) are widely used to study how causal effects vary as a function of economic states, but shock exogeneity alone does not identify this response function. We show that identification follows when the underlying conditional mean is linear in the shock with a state-dependent coefficient, a condition satisfied in canonical micro-macro environments, including first-order perturbation solutions of heterogeneous-agent and macro-finance models.…

行政删失下利用短期替代指标半参数估计延迟结果处理效应AI 解读

The multi-site registry studies, such as Stepped-wedge cluster-randomized trials (SW-CRT), staggered-enrollment RCTs, etc., share a structural feature: the primary long-term outcome is administratively censored for a non-negligible fraction of units, with censoring driven by calendar design rather than by the outcome itself. Standard inverse-probability-of-censoring weighting becomes unstable when observation probabilities $g_Δ$ concentrate near zero for late-crossing units,…

重尾结果下异质极端分位数的因果推断AI 解读

We propose a framework for estimating conditional extreme quantile treatment effects (CEQTEs) in observational studies with heavy-tailed outcomes. Our procedure first estimates intermediate conditional quantiles using inverse-probability-weighted (IPW) quantile regression and then extrapolates them to extreme levels using extreme value theory. Under a linear conditional quantile model, we show that the conditional and marginal distributions of each potential outcome share a…

对称性与因果性：超越独立同分布数据的因果效应识别AI 解读

In the natural sciences, symmetries and cause-effect relationships are ubiquitous. Yet for complex machine-learning tasks, like world-modeling in reinforcement learning, they appear difficult to harness. We propose a formal description of statistical systems based on symmetries in data leaving causal mechanisms invariant. The result is an abstract, simple and general mathematical language for causal reasoning. This paper provides formal descriptions of models and queries,…

异质场所之间分布处理效应的可迁移性AI 解读

We study distributional transportability of treatment effects in a ``cross-site, one-armed target" design, where both treated and control units are observed in a source site, but only control units are observed in a target site. Our object of interest is not estimating the average treatment effect, but recovering the full treated distribution in the target site using transfer knowledge from the source site, while allowing cross-site heterogeneity in measurement systems,…

二元结果配对研究的随机化推断AI 解读

We give an exact randomization-based confidence set for the average treatment effect (ATE) in matched-pair studies with a binary outcome, requiring neither monotonicity nor any distributional assumption beyond the within-pair coin flip. At its core is an analytic solution to the worst-case allocation of attributable effects: two binomial-symmetry lemmas identify the pattern hardest to reject as a single boundary corner, so testing null hypotheses needs no integer program and…

网络中的离策略因果估计AI 解读

In the presence of interference, where the treatment assigned to one unit can affect the outcomes of others, many causal estimands depend on the treatment-assignment policy under which the experiment is conducted. This policy dependence creates a fundamental challenge for off-policy estimation, where the goal is to estimate causal quantities under a hypothetical intervention policy different from the one used to collect data. We study this problem of off-policy estimation of…

COSTA：网络与时间干扰下的协方差优化设计及因果推断AI 解读

Experiments on networks observed over time face network spillovers, temporal carryover, and dependence deliberately introduced by the design. We propose COSTA---Covariance-Optimized Spatiotemporal Treatment Allocation---a joint Bernoulli design for unit--time assignments. Under common treatment marginals and a nonnegative linear network--temporal exposure model, Horvitz--Thompson bias for the sustained all-treated versus all-control contrast is exactly the negative expected…

含不良控制变量的双重差分AI 解读

This paper considers difference-in-differences identification strategies when the parallel trends assumption holds after conditioning on covariates that may themselves be affected by the treatment (often referred to as "bad controls"). We show that common approaches such as simply dropping bad controls are often ill-advised and develop two alternative approaches that allow bad controls to function as genuine controls despite being affected by treatment. First, we derive…

具有平稳有向无环图结构的高维时间序列贝叶斯推断AI 解读

In multivariate time series analysis, understanding the underlying causal relationships among variables is often of interest for various applications. Directed acyclic graphs (DAGs) provide a powerful framework for representing causal dependencies. This paper proposes a novel Bayesian approach for modeling multivariate time series where conditional independencies and causal structure are encoded by a DAG. The proposed model allows structural properties such as stationarity…

哪项政策有效、在哪里有效？双重差分中的州级处理效应估计与推断AI 解读

Policies with a common objective and implementation date may differ in details or context. We distinguish the aggregate average treatment effect on the treated (ATT) from sub-aggregate ATTs defined by implementation cohort, jurisdiction, period, or policy type. UN-DID and DID-INT, two DiD estimators that construct jurisdiction-by-time effects, estimate these ATTs under parallel-trends conditions matched to the aggregation. In CPS placebo-law simulations, randomization…

元理论多重宇宙分析：将理论作为数据以改善研究可靠性AI 解读

Here we present a method for measuring, analyzing and reducing theoretical uncertainty. We call it metatheoretical multiverse analysis (MMA). The method is important because the reliability and replicability of scientific observation and testing of a phenomenon are a function of theoretical uncertainty thus reducing it is a method for improving theory. The MMA method requires encoding theory as propositional logic between variables (nodes) and their relationships with one…

结局缺失下高维线性模型的有效推断AI 解读

This paper is concerned with inference on the regression function of a high-dimensional linear model when outcomes are missing at random. We propose an estimator that combines a Lasso pilot estimate of the regression function with a bias correction term based on the weighted residuals of the Lasso regression. The weights depend on estimates of the missingness probabilities (propensity scores) and solve a convex optimization program that trades off bias and variance…

从控制到处理：超越已观测面板的因果预测AI 解读

Conventional panel data methods recover retrospective counterfactuals within the observed horizon. Many policy decisions, however, are prospective: how would an untreated unit evolve beyond the observed panel under an intervention it has not previously experienced? We develop a causal forecasting framework that combines the counterfactual logic of synthetic controls with multivariate time-series extrapolation. Under a latent factor model, we impose low-rank structure on the…

见树亦见林：BART 的高斯过程极限AI 解读

Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous theoretical work has attempted to explain BART's superior performance by establishing posterior contraction rates for standard BART models, but these rates depend strongly on the number of covariates. Here, we take a different approach and study the behavior of BART as the number of trees grows towards infinity. We show that in this…