每日简报

潜在结果变量因果效应的非参数识别与估计AI 解读

How should researchers conduct causal inference when the outcome of interest is latent and measured imperfectly by multiple indicators? We develop a general nonparametric framework for identifying and estimating average treatment effects on latent outcomes in randomized experiments. We show that latent-outcome estimation faces a fundamental but overlooked noncomparability problem due to measurement-set dependence: adding, removing, or replacing measurement targets a…

更精确的测量还是更大的样本？存在未观测异质性时的政策学习数据采集AI 解读

Empirical research shows that individuals' responses to treatments vary along latent characteristics, such as innate ability or motivation. Therefore, a policymaker seeking to maximize welfare may consider designing policies based on observed characteristics and estimated latent traits. I characterize how the estimates' precision affects the worst-case performance of policies deriving rate-sharp regret bounds for assignment rules that include or exclude them, highlighting…

从联合分析数据学习偏好：结构模型与深度学习的混合方法AI 解读

Conjoint experiments randomize multidimensional profiles, yet political science applications typically report only nonparametric averages that do not recover counterfactual choices or individual tradeoffs. We develop a hybrid structural approach for recovering individual preferences from conjoint data. The estimator combines a flexible machine-learning mean preference function, via a deep netural network in our applications, with respondent-level empirical-Bayes updating in…

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…

条件校准下潜在组别效应的识别AI 解读

We study identification of a structural group effect when the group indicator $G\in\{0,1\}$ is unobserved, but the analyst observes a calibrated probability score $p$ satisfying $E[G\mid p,X]=p$. Under a constant-coefficient structural mean model, the latent-group coefficient $τ$ is point-identified by a closed-form ratio of observable moments whose denominator is the residual score variance $V^{*}=E[(p-E[p\mid X])^2]$. Identification fails exactly when the score is a…

处理组特异非高斯误差下平均处理效应的结构化半参数估计AI 解读

This paper studies average treatment effect (ATE) estimation for continuous outcomes when the treatment-covariate mean is structured but the error distributions are unknown and may differ across treatment arms in scale, skewness, or tail behavior. We introduce a semiparametric model with a finite-dimensional mean, or a prespecified basis expansion, and separate unrestricted additive error laws for treated and control outcomes. The central theoretical contribution is the…

GMM 何时具有 LATE 解释？过度识别工具变量中的权重矩阵与因果含义AI 解读

Under heterogeneous treatment effects, the weighting matrix of overidentified IV-GMM selects the estimand, not just its precision. We characterize the selection exactly: for any parameter-free weighting-matrix map, the GMM estimand is a sum-to-one combination of instrument-specific Wald estimands, with closed-form weights and an exact non-negativity condition; efficient weighting adds a heterogeneity penalty. Continuously updated GMM exits this class through a variance-score…

Under i.i.d. sampling, the maximum score estimator of Manski (1975) converges at the cube-root rate to a non-Gaussian limit. We show that, under multiway dependence and low-level conditions, it is asymptotically normal at a parametric rate in the smallest number of clusters and, when the cluster-level variance is non-degenerate, as fast as regular estimators such as probit. The result rests on a general theory for non-smooth M-estimators, under which a modified bootstrap is…

不让任何人受损：具有遗憾厌恶的政策定向AI 解读

While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice, often due to legal, fairness or cost concerns. We study the problem of policy targeting for a regret-averse planner when training data gives a rich set of observables while the assignment rules can only depend on its subset. Our regret-averse criterion reflects a planner's concern about regret inequality across the population. This, in general,…

涉及半连续暴露的因果推断：两阶段估计AI 解读

Methods for causal inference are well developed for binary and continuous exposures, but in many settings, the exposure has a substantial mass at zero-such exposures are called semi-continuous. We propose a general causal framework for such semi-continuous exposures, together with a novel two-stage estimation strategy. A two-part propensity structure is introduced for the semi-continuous exposure, with one component for exposure status (exposed vs unexposed) and another for…

结合目标学习推断的在线元层级自适应设计：代理结局的评价与利用AI 解读

Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In practice, experimenters often consider multiple candidate designs, each with distinct trade-offs. However, typically only one design is implemented at a time, leaving benefits and costs of alternative designs unobserved and unquantified. To address this, we propose a novel meta-level adaptive…

非参数倾向得分估计的局部平衡校准AI 解读

The propensity score is widely used for causal inference in observational studies, but common parametric estimators can produce biased and inefficient effect estimates when model assumptions are violated. Nonparametric approaches reduce sensitivity to misspecification but often yield unstable weights and inadequate covariate balance. We propose Local Balance with Calibration, implemented by Neural Networks, a weighting method that combines flexible function approximation…

因果基础模型的干预时间序列先验AI 解读

Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time series benchmarks generate observational data with ground-truth causal graphs but lack the interventional data required for training causal foundation models. To address this, we propose \textbf{CausalTimePrior}, a principled…

当“无损一般性的规范化”损失一般性AI 解读

Normalization is ubiquitous in economics, and a growing literature shows that ``normalizations'' can matter for interpretation, counterfactual analysis, misspecification, and inference. This paper provides a general framework for these issues, based on the formalized notion of modeling equivalence that partitions the space of unknowns into equivalence classes, and defines normalization as a WLOG selection of one representative from each class. A counterfactual parameter is…

低阶结局交互干扰下的协变量调整与处理效应估计AI 解读

In randomized experiments, covariates are often used to reduce variance and improve the precision of treatment effect estimates. However, in many real-world settings, interference between units, where one unit's treatment affects another's outcome, complicates causal inference. This raises a key question: how can covariates be effectively used in the presence of interference? Addressing this challenge is nontrivial, as direct covariate adjustment, such as through regression,…

社会科学实验样本流失的共形推断AI 解读

Attrition in survey and field experiments presents a challenge for social science research. Common approaches to deal with this problem -- such as complete case analysis, multiple imputation, and weighting methods -- rely on strong assumptions that may not hold in practice. This paper introduces a new method that combines recent advances in statistical inference with established tools for handling missing data. The approach produces prediction intervals for treatment effects…

级联恒等式：容量约束情境下作为政策参数的 2SLSAI 解读

Governments routinely adjust capacity in rationed programs such as university fields, medical training and public housing, where admitting one individual displaces others and triggers chains of reallocation. We show that in such settings, the standard multi-treatment two-stage least squares (2SLS) coefficient identifies exactly the total societal effect of a marginal expansion, including all downstream reallocations. The result is an algebraic identity: under instrument…

平均处理效应的高效协作学习AI 解读

In response to the growing need for generating real-world evidence from multi-site collaborative studies, we introduce an efficient collaborative learning approach to evaluate average treatment effect (ECO-ATE) in a multi-site setting under data sharing constraints. Specifically, ECO-ATE operates in a federated manner, using individual-level data from a user-defined target population and summary statistics from other source populations, to construct efficient estimator for…