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向上秩流动曲线的非参数与半参数估计AI 解读

We introduce the upward rank mobility curve as a new measure of intergenerational mobility that captures upward movements across the entire parental income distribution. Our approach extends Bhattacharya and Mazumder (2011) by conditioning on a single parental income rank, thereby eliminating aggregation bias. We show that the measure can be characterized solely by the copula of parent and child income, and we propose a nonparametric copula-based estimator with better…

模型孪生随机化：重复个体处理效应的估计AI 解读

Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on…

动态离散选择模型中的连续永久不可观测异质性AI 解读

In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity. However, consistent estimation typically requires both restrictions on the support of unobserved heterogeneity and a high-level injectivity condition that is difficult to verify. This paper provides primitive conditions for point identification of a broad class of DDC models with multivariate continuous permanent unobserved heterogeneity. The results apply…

利用观测数据评价处理获益预测器：识别与混杂偏差AI 解读

A treatment benefit predictor (TBP) is a function that maps patient characteristics to an estimate of the treatment benefit for that patient. Such predictors support optimizing individualized treatment decisions, which are central to precision medicine. However, evaluating the predictive performance of a TBP is challenging, as this often must be conducted in a sample where treatment assignment is not random. After briefly reviewing several metrics for evaluating TBPs, we…

目标研究缺少处理数据时的平均处理效应估计AI 解读

Researchers are frequently interested in understanding the causal effect of treatment interventions. However, in some cases, the treatment of interest--readily available in a randomized controlled trial (RCT)--is either not directly measured or entirely unavailable in observational datasets. This challenge has motivated the development of stochastic incremental propensity score interventions which operate on post-treatment exposures affected by the treatment of interest with…

SensIAT：评估时间不规则的随机试验敏感性分析 R 包AI 解读

This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of inference involve the population mean outcome in each treatment arm as well as the difference in these means (i.e., treatment effect) at specified times after randomization. This methodology is useful in settings where there is concern that…

高维数据下利用外部对照估计 ATT：保留双重稳健性并确保双重安全性AI 解读

Randomized controlled trials (RCTs) are widely regarded as the gold standard for causal inference in biomedical research. For instance, when estimating the average treatment effect on the treated (ATT), a doubly robust estimation procedure can be applied, requiring either the propensity score model or the control outcome model to be correctly specified. In this paper, we address scenarios where external control data, often with a much larger sample size, are available. Such…

微随机试验中调节型因果游离优势比的稳健估计AI 解读

Micro-randomized trials (MRTs) have become increasingly popular for developing and evaluating mobile health interventions that promote healthy behaviors and manage chronic conditions. The recently proposed causal excursion effects have become the standard measure for interventions' marginal and moderated effect in MRTs. Existing methods for MRTs with binary outcomes focus on causal excursion effects on the relative risk scale. However, a causal excursion effect on the odds…

反事实余循环：稳健且一致的反事实传输框架AI 解读

Estimating joint distributions (a.k.a. couplings) over counterfactual outcomes is central to personalized decision-making and treatment risk assessment. Two emergent frameworks with identifiability guarantees are: (i) bijective structural causal models (SCMs), which are flexible but brittle to mis-specified latent noise; and (ii) optimal-transport (OT) methods, which avoid latent noise assumptions but can produce incoherent counterfactual transports which fail to identify…

随机试验中分配对因果效应定义与识别的作用AI 解读

In randomized trials, the per-protocol effect, that is, the effect of being assigned a treatment strategy and receiving treatment according to the assigned strategy, is sometimes thought to reflect the effect of the treatment strategy itself, without intervention on assignment. Here, we argue by example that this is not necessarily the case. We examine a causal structure for a randomized trial where these two causal estimands -- the per-protocol effect and the effect of the…

存在样本选择的配对关系回归AI 解读

This paper addresses the sample selection problem in panel dyadic regression analysis. Dyadic data often include many zeros in the main outcomes due to the underlying network formation process. This not only contaminates popular estimators used in practice but also complicates the inference due to the dyadic dependence structure. We extend Kyriazidou (1997)'s approach to dyadic data and characterize the asymptotic distribution of our proposed estimator. The convergence rates…

外科干预的因果机器学习AI 解读

Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spinal fusion or scoliosis correction, accurate estimation of individualized treatment effects (ITEs) remains limited due to the reliance on traditional statistical methods that struggle with complex, heterogeneous data. In this study, we develop a multi-task meta-learning framework, X-MultiTask, for…

多变量时间序列复合极端事件的因果尾部系数AI 解读

Extreme events are often multivariate in nature. A compound extreme occurs when a combination of variables jointly produces a significant impact, even if individual components are not necessarily marginally extreme. Compound extremes have been observed across a wide range of domains, including space weather, climate, and environmental science. For example, heavy rainfall sustained over consecutive days can impose cumulative stress on urban drainage systems, potentially…

语言模型作为因果效应生成器AI 解读

In this work, we present sequence-driven structural causal models (SD-SCMs), a framework for specifying causal models with user-defined structure and language-model-defined mechanisms. We characterize how an SD-SCM enables sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. We then leverage this procedure to propose a new type of benchmark for causal inference methods, generating individual-level…

贝叶斯多元密度-密度回归AI 解读

We introduce a novel and scalable Bayesian framework for multivariate-density-density regression (DDR), designed to model relationships between multivariate distributions. Our approach addresses the critical issue of distributions residing in spaces of differing dimensions. We utilize a generalized Bayes framework, circumventing the need for a fully specified likelihood by employing the sliced Wasserstein distance to measure the discrepancy between fitted and observed…

不必完全排除：使用半工具变量开展因果推断AI 解读

This paper proposes semi-instrumental variables (semi-IVs) as an alternative to instrumental variables (IVs) to identify the causal effect of a binary (or discrete) endogenous treatment. A semi-IV is a less restrictive form of instrument: it affects the selection into treatment but is excluded only from one, not necessarily both, potential outcomes. Having two continuously distributed semi-IVs, one excluded from the potential outcome under treatment and the other from the…

马尔可夫结构因果模型的规范表示：反事实推理框架AI 解读

Counterfactual reasoning aims at answering contrary-to-fact questions like ``Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified-even by randomized experiments-they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In…

HP-ACCORD：临床多组学研究中的大规模偏相关网络学习AI 解读

Graphical model estimation from multi-omics data requires a balance between statistical estimation performance and computational scalability. We introduce a novel pseudolikelihood-based graphical model framework that reparameterizes the target precision matrix while preserving the sparsity pattern and estimates it by minimizing an $\ell_1$-penalized empirical risk based on a new loss function. The proposed estimator maintains estimation and selection consistency in various…

协变量分布偏移下的自动去偏机器学习AI 解读

We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment modifying surrogate covariates used to predict long-term outcomes. Another example is the average treatment effect for a population with a shifted covariate distribution, like the effect of…

利用企业调查、最大熵与带状多面体估计产业异质性AI 解读

This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data…

风险比、优势比与风险差：哪种因果效应尺度更易推广？AI 解读

There are many measures to report so-called treatment or causal effects: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, e.g. absolute versus relative, is often debated because it leads to different impressions of the benefit or risk of a treatment. Besides, different causal measures may lead to various treatment effect heterogeneity: some input variables may have an influence on some causal measures and no effect at all on…

协变量平衡与平均处理效应加权、双重稳健估计量的等价性AI 解读

How should researchers adjust for covariates? We show that if the propensity score is estimated using a specific covariate balancing approach, inverse probability weighting (IPW), augmented inverse probability weighting (AIPW), and inverse probability weighted regression adjustment (IPWRA) estimators are numerically equivalent for the average treatment effect (ATE), and likewise for the average treatment effect on the treated (ATT). The resulting weights are inherently…

稳定隐性混杂与二元结果的准工具变量方法AI 解读

Instrumental variable (IV) methods are central to causal inference from observational data, particularly when a randomized experiment is not feasible. However, of the three conventional core IV identification conditions, only one, IV relevance, is empirically verifiable; often one or both of the other conditions, exclusion restriction and IV independence from unmeasured confounders, are unmet in real-world applications. These challenges are compounded when the outcome is…

细化双重差分研究中的无预期效应概念AI 解读

We address an ambiguity in identification strategies using difference-in-differences, which are widely applied in empirical research, particularly in economics. The assumption commonly referred to as the "no-anticipation assumption" states that treatment has no effect on outcomes before its implementation. However, because standard causal models rely on a temporal structure in which causes precede effects, such an assumption seems to be inherently satisfied. This raises the…