数据链接：
官方服务：
资源简介：
Data for Causal Experiments (Behavior)
应用场景：
创建时间：
2025-09-10
相关数据集
Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials
In clinical trials, it is often of interest to understand the principal causal effect (PCE), the average treatment effect for a principal stratum (a subset of patients defined by the potential outcome
DataCite Commons2023-02-14 更新70
Estimated direct effects, indirect effects and total effects of exogenous and endogenous variable on CS delivery.
Estimated direct effects, indirect effects and total effects of exogenous and endogenous variable on CS delivery.
NIAID Data Ecosystem20
Replication Data for: Bayesian versus maximum likelihood estimation of treatment effects in bivariate probit instrumental variable models
Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum l
NIAID Data Ecosystem10
DeepMR estimates causal effects (CE) accurately with high coverage.
Accuracy is R2. Local corresponds to CEs for individual regions, global for the meta-analysis mean. For global CE accuracy and coverage the first value comes from using MR-Egger and the second from th
NIAID Data Ecosystem40
