Statisticians show growing interest in estimating and analyzing heterogeneity in causal
effects in observational studies. However, there usually exists a trade-off between accuracy …

The exploration of causal relationships between treatments and outcomes, and the
estimation of causal effects from observational data, have garnered considerable interest in …

Efforts to estimate treatment effects and draw causal inferences based on observational data
are increasingly relevant with the abundance of such data in the social and behavioral …

Estimating heterogeneous treatment effects has become increasingly important in many
fields: for example, they are required to select a personalized treatment for a patient, which …

Estimating how a treatment affects different individuals, known as heterogeneous treatment
effect estimation, is an important problem in empirical sciences. In the last few years, there …

There is great demand for inferring causal effect heterogeneity and for open-source
statistical software, which is readily available for practitioners. The mcf package is an open …

With the rise of large and fine-grained data sets, there is a desire for researchers,
physicians, businesses, and policymakers to estimate the treatment effect heterogeneity …

In classical study designs, the aim is often to learn about the effects of a treatment or
intervention on a single outcome; in many modern studies, however, data on multiple …

Researchers in many fields endeavor to estimate treatment effects by regressing outcome
data (Y) on a treatment (D) and observed confounders (X). Even absent unobserved …

Assessing treatment effects in observational studies is a multifaceted problem that not only
involves heterogeneous mechanisms of how the treatment or cause is exposed to subjects …