Dose Response

Dose-response analysis aims to quantify the relationship between the amount of a treatment (e.g., drug dosage, aid disbursement) and its effect on an outcome. Current research heavily emphasizes estimating heterogeneous dose-response curves, accounting for individual differences and unobserved confounders, often employing machine learning methods such as Gaussian processes, variational autoencoders, and causal forests, along with techniques like optimal transport and propensity score weighting. These advancements are crucial for personalized medicine, policy optimization (e.g., resource allocation), and other fields requiring precise understanding of treatment effects across diverse populations and treatment levels. The development of robust and scalable methods for handling continuous treatments and high-dimensional data is a major focus.