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Principal stratification (PS) is a commonly used approach for understanding the mechanisms through which a treatment affects an outcome. The goal of this work is to extend the PS framework to studies with continuous treatments, which introduces a number of both challenges and opportunities in terms of defining causal effects and performing inference. This manuscript provides multiple key methodological contributions: 1) we introduce principal causal estimands for continuous…

时空干扰下用于实验的聚类切换设计AI 解读

We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We suppose spatial interference is described by a graph, where a unit's outcome depends on its neighborhood's treatments, and that temporal interference is…

This article develops an identification framework for heterogeneous policy effects when individual-level data are available only in the treated jurisdiction. The framework combines outcome changes from repeated cross-sections within that jurisdiction with a population-average effect identified from aggregate panel data. Under conditional relative parallel trends and overlap, this information identifies subgroup effects standardized to the treated jurisdiction's overall…

Mendelian randomization (MR) infers causal effects of exposures on outcomes using genetic variants as instrumental variables, but existing methods remain vulnerable to bias and loss of power when instruments are invalid. We hypothesize that closely related outcome traits share a large overlap in their valid instruments for a given exposure, so that borrowing instruments across such traits can yield more robust MR inference. To operationalize this, we introduce a novel…

预期高安慰剂反应时安慰剂对照随机试验的因果效应估计AI 解读

In placebo-controlled randomized clinical trials (RCTs), the placebo response significantly modifies treatment effects and diminishes the intention-to-treat (ITT) treatment effect, $Δ_{ITT}$. This study presents a novel two-stage framework for estimating the standardized causal treatment effect, $Δ_{STD}$, among the ITT population, under the assumption that their placebo responses are similar to the levels of self-administering medication at home. The first stage employs a…

基于广义逆距离权重的GNAR模型改进多元时间序列预测AI 解读

Many multivariate time series arise on networks where relationships between variables depend on physical or more general distances. Standard vector autoregressive models struggle in such settings due to their quadratic parameter growth, while generalised network autoregressive (GNAR) models offer a parsimonious alternative by explicitly incorporating network structures. Existing GNAR formulations assume fixed weights, typically uniform or reciprocal distances, which may not…

使用生成模型检验条件分布相等AI 解读

Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test…

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on…

We develop a semiparametric continuous time inferential framework for testing cumulative treatment effects, including both immediate and carryover effects. The latent state process is modeled by an ordinary differential equation (ODE) whose drift depends on state and treatment, while the observed data are noisy measurements of this latent state at irregular, multi resolution time points. We define the average treatment effect (ATE) as the difference of expected cumulative…

Governments and development partners often seek Sustainable Development Goal (SDG) "accelerators" whose progress can propagate gains across human well-being, institutions, resources, and environmental priorities. Such claims require evidence on direction, timing, robustness, and trade-offs, not only network connectivity. We map directed SDG interdependence using a balanced panel of 114 countries observed annually from 2000 to 2024. Because the goal series are persistent,…