We consider the problem of estimating a dose-response curve, both globally and locally at a
point. Continuous treatments arise often in practice, eg in the form of time spent on an …

Studying causal effects of continuous treatments is important for gaining a deeper
understanding of many interventions, policies, or medications, yet researchers are often left …

Inverse probability of treatment weighting (IPTW) is a popular method for estimating the
average treatment effect (ATE). However, empirical studies show that the IPTW estimators …

Estimating treatment effects has numerous real-world applications in various fields, such as
epidemiology and political science. While much attention has been devoted to addressing …

In many real-world causal inference applications, the primary outcomes (labels) are often
partially missing, especially if they are expensive or difficult to collect. If the missingness …

We identify the average dose–response function (ADRF) for a continuously valued error-
contaminated treatment by a weighted conditional expectation. We then estimate the …

Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-
optimal estimators for a large class of low-dimensional (smooth) statistical …

Semiparametric efficient estimation of various multi-valued causal effects, including quantile
treatment effects, is important in economic, biomedical, and other social sciences. Under the …

We propose a general framework for the specification testing of continuous treatment effect
models. We assume a general residual function, which includes the average and quantile …

Causal inference is widely used in various fields, such as biology, psychology, and
economics, etc. In observational studies, balancing the covariates is an important step in …