每日简报

回归不连续设计中直接估计条件平均处理效应的贝叶斯加性回归树AI 解读

Regression discontinuity designs (RDD) are widely used for causal inference. In many empirical applications, treatment effects vary substantially with covariates, and ignoring such heterogeneity can lead to misleading conclusions, which motivates flexible modeling of heterogeneous treatment effects in RDD. To this end, we propose a Bayesian nonparametric approach to estimating heterogeneous treatment effects based on Bayesian Additive Regression Trees (BART). The key feature…

从大语言模型中提取因果关系假说AI 解读

Large language models (LLMs) are trained on enormous amounts of data and encode knowledge in their parameters. We propose a pipeline to elicit causal relationships from LLMs. Specifically, (i) we sample many documents from LLMs on a given topic, (ii) we extract an event list from from each document, (iii) we group events that appear across documents into canonical events, (iv) we construct a binary indicator vector for each document over canonical events, and (v) we estimate…

利用外部对照开展试验近端学习：HIV 预防案例AI 解读

With the advent of effective pre-exposure prophylaxis agents, active-controlled HIV prevention trials have become a common study design. Nevertheless, estimating absolute efficacy relative to a placebo remains important. In this paper, we introduce a novel application of proximal causal inference methods to estimate the counterfactual cumulative HIV incidence under placebo for participants in an active-controlled trial of cabotegravir, using external control data from a…

处理效应完全中介与因果机制可识别性的检验AI 解读

In causal analysis, understanding the causal mechanisms through which an intervention or treatment affects an outcome is often of central interest. We propose a test to evaluate (i) whether the causal effect of a treatment that is randomly assigned conditional on covariates is fully mediated by, or operates exclusively through, observed intermediate outcomes (referred to as mediators or surrogate outcomes), and (ii) whether the various causal mechanisms operating through…

通过状态空间边际化高效进行动态结构方程模型的贝叶斯估计AI 解读

Dynamic structural equation models (DSEMs) combine time-series modeling of within-person processes with hierarchical modeling of between-person differences and differences between timepoints, and have become very popular for the analysis of intensive longitudinal data in the social sciences. An important computational bottleneck has, however, still not been resolved: whenever the underlying process is assumed to be latent and measured by one or more indicators per timepoint,…

时变协变量下交错双重差分处理效应的双重稳健估计AI 解读

With staggered adoption, difference-in-differences (DiD) analyses define the average treatment effect on the treated (ATT) at the group-period level and aggregate it into groupwise, periodwise, dynamic, and overall effects. We study a period-by-period version of conditional parallel trends, with covariates unaffected by treatment while allowing covariates to shift the trends in untreated potential outcomes. We propose an augmented inverse probability weighting (AIPW)…

复合Lp分位数回归、近分位数回归与Oracle模型选择理论AI 解读

In this paper, we consider high-dimensional Lp-quantile regression which only requires a low order moment of the error and is also a natural generalization of the above methods and Lp-regression as well. The loss function of Lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss function requiring the finiteness of error's variance and thus promises excellent properties of Lp-quantile regression.…

一般混杂下线性非高斯因果模型的参数识别AI 解读

Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent variables. Our focus is on the commonly studied acyclic setting, where each model corresponds to a directed acyclic graph (DAG). For this case, prior literature has demonstrated that connections to overcomplete independent component analysis…

量化贸易与空间模型中的测量误差和反事实AI 解读

Counterfactuals in quantitative trade and spatial models are functions of the current state of the world and the model parameters. Common practice treats the current state of the world as perfectly observed, but there is good reason to believe that it is measured with error. This paper provides tools for quantifying uncertainty about counterfactuals when the current state of the world is measured with error. I recommend an empirical Bayes approach to uncertainty…

连续处理的异质性响应：基于聚类的因果框架AI 解读

When treatments are non-randomly assigned, continuous, and yield heterogeneous effects at the same intensity, causal identification becomes particularly challenging. In such contexts, existing approaches often fail to provide policy-relevant estimates of the relationship between treatment intensity and outcomes, especially in the presence of limited common support. To fill this gap, we introduce the Clustered Dose-Response Function (Cl-DRF), a novel estimator designed to…

用贝叶斯层级可调随机分区识别自适应富集试验中的治疗效应异质性AI 解读

Treatment effect heterogeneity refers to the systematic variation in treatment effects across subgroups. There is an increasing need for clinical trials that aim to investigate treatment effect heterogeneity and estimate subgroup-specific responses. While several statistical methods have been proposed to address this problem, existing partitioning-based methods often depend on auxiliary analysis, overlook model uncertainty, or impose inflexible borrowing strength. We propose…