In the era of fast-paced precision medicine, observational studies play a major role in
properly evaluating new treatments in clinical practice. Yet, unobserved confounding can …

Experimental and observational studies often lack validity due to untestable assumptions.
We propose a double machine learning approach to combine experimental and …

In this paper, we review recent advances in statistical methods for the evaluation of the
heterogeneity of treatment effects (HTE), including subgroup identification and estimation of …

Individualized treatment decisions can improve health outcomes, but using data to make
these decisions in a reliable, precise, and generalizable way is challenging with a single …

We propose a general class of algorithms for estimating heterogeneous treatment effects on
multiple studies. Our approach, called the multi-study R-learner, generalizes the R-learner to …

Driven by the need to generate real-world evidence from multi-site collaborative studies, we
introduce an efficient collaborative learning approach to evaluate average treatment effect in …

We study Federated Causal Inference, an approach to estimate treatment effects from
decentralized data across centers. We compare three classes of Average Treatment Effect …

Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates
with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing …

Assessing treatment effect moderation is critical in biomedical research and many other
fields, as it guides personalized intervention strategies to improve participant's outcomes …

In public health, it is critical for policymakers to assess the relationship between the disease
prevalence and associated risk factors or clinical characteristics, facilitating effective …