每日简报

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…

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…

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…

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…

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…

非可分第一阶段下加性可分三角模型的闭式估计与一致推断AI 解读

This paper studies the nonparametric identification and estimation of additively separable triangular models with continuous endogenous and instrumental variables, allowing for a nonseparable first-stage equation. Under the independence of instrumental variables and unobservables, we show that the outcome function possesses a closed-form expression as a functional of conditional cumulative distribution functions. The resulting plug-in estimators require no regularization and…

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…

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…

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…

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…

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…

加速住宅太阳能系统采用：动态结构模型的政策分析AI 解读

Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emissions. Governments and municipalities encourage household photovoltaic (PV) adoption through upfront rebates and tax credits. Limited budgets require principled, data-driven policies that account for the drivers of adoption and the effects of incentives on adoption rates. Methodology/results: We develop a dynamic structural…

正值性违反下修正处理政策的因果效应：部分识别方法AI 解读

Modified treatment policies (MTPs) are interventions based on each individual's natural treatment value. We study mean outcomes under MTPs for continuous treatments, including exposure mixtures. Positivity is the standard sufficient condition for identifying these mean outcomes without extrapolation: policy-generated values remain supported given covariates. With multivariate treatments or continuous covariates, treatment--covariate combinations can be sparse or unsupported.…

固定时间维度随机系数动态面板模型的估计AI 解读

We study dynamic linear panel data models in which the lagged outcomes and strictly exogenous covariates carry individual-specific coefficients and the time-varying errors have a flexible covariance structure. With a fixed number of time periods, we point-identify the joint distribution of the random coefficients and the structural errors under a distributional form of strict exogeneity, and propose a closed-form, multi-step estimator based on the inverse Radon transform. We…

强化 CUPED：切换实验中的多变量协变量调整AI 解读

Controlled-experiment Using Pre-Experiment Data (CUPED) reduces the variance of the treatment effect estimator in online experiments by adjusting the in-experiment outcome metric using its lagged pre-experiment value. This method can be strengthened by enriching its covariate set while keeping it automatable and guarding against overfitting and covariate leakage. In switchback and related clustered experiment designs, this enrichment may be especially fruitful since…

用于动态神经潜在嵌入的潜在空间网络模型AI 解读

We introduce a novel latent space network model for analyzing multivariate time series of neural spike-train data. The methodology is motivated by an experimental study in mice, where neuronal responses were collected under a sequence of visual discrimination tasks. We adopt a latent variable framework to model the firing rates of aggregated brain areas, while simultaneously inferring the interactions between regions via a hidden network structure. This interaction network…

利用稳定平衡权重对多种目标参数进行简便协变量调整AI 解读

Covariate adjustment can improve precision in estimating treatment effects in clinical trials, and regulatory guidance increasingly encourages its implementation. Despite its benefits, covariate adjustment can require advanced statistical techniques or tailored approaches for different estimands, which can deter its use in practice. To address this issue, we introduce a simplified approach to covariate adjustment using stable balancing weights that applies directly across…

SAUSS：有限因变量模型的无偏模拟得分随机逼近AI 解读

Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fixed per-observation simulation budget, simulated maximum likelihood introduces simulation bias, while each optimization step requires a full-sample likelihood evaluation. We propose Stochastic Approximation with Unbiased Simulated Scores (SAUSS), an averaged stochastic approximation based on conditionally unbiased mini-batch…

双重差分中以研究范围换取可信度AI 解读

When parallel trends fails for some treated cohorts but not others, the average treatment effect on the treated (ATT), an average over all of them, is exactly the target that becomes hard to recover. We propose changing the estimand rather than defending it. The credible-subpopulation local ATT (LATT) is the effect for the subpopulation of cohorts whose parallel trends is credible, and it is point-identified under parallel trends for the selected cohorts alone, a weaker…

部分识别离散响应模型的非参数贝叶斯推断AI 解读

This paper proposes a nonparametric Bayesian inference framework for partially identified discrete response models. The key observation is that these models map a reduced-form conditional choice probability to an identified set. Consequently, nonparametric Bayesian inference for the conditional probability mass function leads to Bayesian inference for the identified set. The inference framework nests conditional moment inequalities and linear systems with unknown…

存在干扰时网络实验的最优设计AI 解读

Experimental design under network interference is challenging because outcomes may depend on the treatment assignments of neighboring units. Existing approaches account for network structure but are typically assessed on small or simplified networks, limiting their applicability to complex real-world settings. We propose a network-aware treatment allocation framework that jointly accounts for allocation balance and network topology via an optimality criterion based on the…

处理—混杂反馈下电力需求的边际结构模型与连续处理自适应 LASSOAI 解读

Temperature is the most important meteorological driver of electricity demand, but its causal effect has not been estimated under the time-varying confounding that characterizes weather processes. Current temperature is associated with weather conditions, including precipitation, snow cover, and cloud cover, that also influence electricity demand, and past temperature shapes future weather, producing treatment-confounder feedback under which standard regression adjustment is…

混合对照试验的功效与样本量计算AI 解读

Hybrid controlled trials (HCTs) augment randomized controls with external controls (ECs) to address practical challenges in randomized controlled trials (RCTs) and improve statistical power in settings such as rare diseases, oncology, and pediatrics. However, prospective sample-size determination is challenging because the required RCT sample size depends on the comparability of ECs with RCT controls, which are unavailable at the planning stage. We propose a 5+3 design for…

提高检验功效的统一自适应富集设计AI 解读

Randomized controlled trials (RCTs) are the gold standard for evaluating treatment effects, but fixed eligibility criteria and enrollment decisions can be inefficient, especially when treatment effects vary across patient subpopulations. Adaptive enrichment trials update enrollment using interim data to improve efficiency. Enrichment methods are developed for two settings: prespecified subgroups, and continuous covariates where enrollment is guided by a learned cutoff. Many…