Abstract: In the context of disease progression analysis, estimating the causal effect of a time-continuous treatment assigned to a given population is an important problem. This is also relevant in many applications that gather massive high-dimensional and time-dependent data structures for understanding causal relationships, e.g., in biomedicine and financial markets. In this work, we propose a nonparametric model for event processes, based on a linear combination of tensor products of kernel functions, composed of indicator functions of the event processes observed up to a fixed time. Finite-sample guarantees for the estimation and prediction errors are proved, that yield data-driven optimal weights for the lasso penalty term, and allow for heteroscedastic expansions. This is a joint work with Prof. Niels R. Hansen (Copenhagen University, Denmark).

Host: Prof. Deborah Sulem