nhejazi/haldensify: Highly Adaptive Lasso Conditional Density Estimation

An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with sieve estimation (undersmoothing) of the generalized propensity score to attain the semi-parametric efficiency bound (per Hejazi, Benkeser, Díaz, and van der Laan <doi:10.48550/arXiv.2205.05777>).

install.packages("remotes")
remotes::install_github("nhejazi/haldensify")