map_hazard_to_density

Map Predicted Hazard to Predicted Density for a Single Observation

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
undersmoothing of the generalized propensity score estimator to attain the
semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan
<doi:10.48550/arXiv.2205.05777>).

Nima Hejazi

haldensify

Highly Adaptive Lasso Conditional Density Estimation

David Benkeser

Mark van der Laan

Rachael Phillips

map_hazard_to_density function

<dl><dt>hazard_pred_single_obs</dt>
<dd>A <code>numeric</code> vector of predicted hazard of
failure in a given bin (under a given partitioning of the support) for a
single observational unit based on a long format data structure (from
<code>format_long_hazards</code>). This is the probability that a given
value falls in a corresponding bin, given that it has not yet failed
(fallen in a preceding bin), as per diaz2011superhaldensify.</dd></dl>

Arguments

Map Predicted Hazard to Predicted Density for a Single Observation — map_hazard_to_density

<dl>

<dt>hazard_pred_single_obs</dt>
<dd>A <code>numeric</code> vector of predicted hazard of
failure in a given bin (under a given partitioning of the support) for a
single observational unit based on a long format data structure (from
<code>format_long_hazards</code>). This is the probability that a given
value falls in a corresponding bin, given that it has not yet failed
(fallen in a preceding bin), as per diaz2011superhaldensify.</dd>

</dl>

map_hazard_to_density: Map Predicted Hazard to Predicted Density for a Single Observation

Description

Usage

Value

Arguments

Details