overlap weighted average treatment effect on the treatedThe use of propensity score methods has become ubiquitous in causal inference. At the heart of these methods is the positivity assumption. Violation of the positivity assumption leads to the presence of extreme propensity score weights when ...
Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projecti
From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate p... Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs ...
hdidregress — Heterogeneous difference in differences Description Options Acknowledgments Quick start Remarks and examples References Menu Stored results Also see Syntax Methods and formulas Description hdidregress estimates average treatment effects on the treated (ATETs) that may vary over time and over...
Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatm...
RA estimators use a two-step approach to estimating treatment effects: 1. They fit separate regression models of the outcome on a set of covariates for each treatment level. 2. They compute the averages of the predicted outcomes for each subject and treatment level. These averages reflect the...
In addition, we extend our analysis to other estimands, including ATE in the source population and the average treatment effect on treated (ATT) in both the source and target populations. Furthermore, we empirically validate our findings by constructing locally efficient estimators and conducting ...
Whereas the variance of the average treatment effect is unaffected by knowledge of the propensity score, the bound for the treatment effect on the treated changes if the propensity score is known. However, the reasons for this remain unclear. In this paper it is shown that knowledge of the ...
Whereas the variance of the average treatment effect is unaffected by knowledge of the propensity score, the bound for the treatment effect on the treated changes if the propensity score is known. However, the reasons for this remain unclear. In this paper it is shown that knowledge of the ...
DeepLearningCausal is an R package that provides functions to estimate the Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) weighted ensemble method and Deep Neural Networks....