haldensify: Highly adaptive lasso conditional density estimation in R
Nima S. Hejazi, Mark J. van der Laan, David Benkeser
Abstract
The haldensify R package serves as a toolbox for nonparametric conditional density estimation based on the highly adaptive lasso, a flexible nonparametric algorithm for the estimation of functional statistical parameters (e.g., conditional mean, hazard, density). Building upon an earlier proposal (Dz & van der Laan, 2011), haldensify leverages the relationship between the hazard and density functions to estimate the latter by applying pooled hazard regression to a synthetic repeated measures dataset created from the input data, relying upon the framework of cross-validated loss-based estimation to yield an optimal estimator (Dudoit & van der Laan, 2005; van der While conditional density estimation is a fundamental problem in statistics, arising naturally in a variety of applications (including machine learning), it plays a critical role in estimating the causal effects of continuous-or ordinal-valued treatments. In such settings this covariate-conditional treatment density has been termed the generalized propensity score