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 non/semi-parametric estimation of the conditional density, the highly adaptive lasso, a nonparametric regression function shown to reliably estimate a large class of functions at a fast convergence rate, is utilized. The pooled hazards formulation implemented was first described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To complement the conditional density estimation utilities, nonparametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using the conditional density estimation procedure to estimate the generalized propensity score. Per Hejazi, Benkeser, Díaz, and van der Laan <>10.48550/arXiv.2205.05777>, these nonparametric IPW estimators can be coupled with sieve estimation (undersmoothing) of the generalized propensity score estimators to attain the non/semi-parametric efficiency bound.

Last updated 7 months ago

causal-inferenceconditional-density-estimatesdensity-estimationhighly-adaptive-lassoinverse-probability-weightsmachine-learningnonparametric-regressionpropensity-score

16 stars 2.24 score 55 dependencies 2 dependents

txshift - Efficient Estimation of the Causal Effects of Stochastic Interventions

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in 'sl3', available for download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Last updated 11 months ago

causal-effectscausal-inferencecensored-datamachine-learningrobust-statisticsstatisticsstochastic-interventionsstochastic-treatment-regimestargeted-learningtreatment-effectsvariable-importance

13 stars 1.76 score 59 dependencies

nima - Nima Hejazi's R Toolbox

Miscellaneous R functions developed as collateral damage over the course of work in statistical and scientific computing for research. These include, for example, utilities that supplement existing idiosyncrasies of the R language, extend existing plotting functionality and aesthetics, help prepare data objects for imputation, and extend access to command line tools and systems-level information.

Last updated 5 years ago

0.73 score 38 dependencies

biotmle - Targeted Learning with Moderated Statistics for Biomarker Discovery

Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.

Last updated 4 months ago

regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncology

0.71 score 101 dependencies