Causal inference with misspecified exposure mappings: separating definitions and assumptions

网络干扰实验中平均处理效应 Horvitz-Thompson 估计量的数据自适应暴露阈值

Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference

针对网络干扰实验中暴露映射阈值难以确定的问题，本文提出一种基于线性剂量-反应模型的数据自适应方法，通过最小化 Horvitz-Thompson 估计量的均方误差来选择最优 h 比例阈值，并在模拟和 Amazon 产品相似性图上验证了其优于非自适应选择且对模型偏差具有稳健性。

Causal inference under over-simplified longitudinal causal models

Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether - and how - causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted av…

网络结构方程模型用于因果中介和溢出效应

Network Structural Equation Models for Causal Mediation and Spillover Effects

本文提出了一种基于网络结构方程模型的方法，用于分析因果中介和溢出效应，通过理论框架和实证研究验证了其在复杂网络数据中的有效性。

Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including: 1) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be 2) multivariate, and 3) the existence of multivariate mediators. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no estimator for IDE/I…

Causal Inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single MCMC, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We show that our proposed variance…

网络中的离策略因果估计

Off-policy causal estimation in networks

针对网络干扰下的离策略因果估计难题，本文基于傅里叶基构建最小L²权重，推导鲁棒性界、一致性条件与方差估计量，通过仿真验证其在实验设计分析中的有效性。

如何在基于模型的阶梯楔形试验中实现模型稳健推断？

How to achieve model-robust inference in stepped wedge trials with model-based methods?

本研究针对阶梯楔形试验中基于模型的分析方法在模型误设下的性质未被系统探究的问题，证明了边际治疗效应估计一致性的条件，提出结合三明治方差估计与g-computation实现模型稳健推断，并通过模拟和实例验证了结论。

为何因果推断中的双重稳健估计器对方差估计不具有双重稳健性？

Why Is the Double-Robust Estimator for Causal Inference Not Doubly Robust for Variance Estimation?

本文探讨了因果推断中双重稳健估计器在方差估计中的局限性，并提出混合框架方法以实现有效推断。

基于图神经网络的网络数据因果中介分析

Causal Mediation Analysis for Network Data with Graph Neural Network

针对网络人群因果中介分析的无干扰假设缺陷，开发含处理与中介溢出效应的非参数框架，用图神经网络学习扰动函数，经模拟和中国农村农业保险实验验证了方法有效性。