Background: Policy evaluation studies that assess how state-level polici...
Real-world data, such as administrative claims and electronic health rec...
We develop flexible and nonparametric estimators of the average treatmen...
Individualized treatment decisions can improve health outcomes, but usin...
An important strategy for identifying principal causal effects, which ar...
Estimating treatment effects conditional on observed covariates can impr...
In epidemiology and social sciences, propensity score methods are popula...
Methods for extending – generalizing or transporting – inferences from a...
While there is an emergence of research investigating the educational im...
Matching and weighting methods for observational studies require the cho...
Objective. Understanding how best to estimate state-level policy effects...
In multicenter randomized trials, when effect modifiers have a different...
The academic, socioemotional, and health impacts of school policies
thro...
This paper aims to contribute to helping practitioners of causal mediati...
Background: Guidelines and recommendations from public health authoritie...
Background: Randomized controlled trials are often used to inform policy...
Causal mediation analysis is complicated with multiple effect definition...
To limit the spread of the novel coronavirus, governments across the wor...
Policy responses to COVID-19, particularly those related to
non-pharmace...
Over the last two decades, there has been a surge of opioid-related over...
Randomized trials are considered the gold standard for estimating causal...
Causal inference analyses often use existing observational data, which i...
We address measurement error bias in propensity score (PS) analysis due ...
Many lifestyle intervention trials depend on collecting self-reported
ou...
The incorporation of causal inference in mediation analysis has led to
t...
When variables that are treatment effect modifiers also influence the
de...