lmtp - Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies
Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).
Last updated 6 days ago
causal-inferencecensored-datalongitudinal-datamachine-learningmodified-treatment-policynonparametric-statisticsprecision-medicinerobust-statisticsstatisticsstochastic-interventionssurvival-analysistargeted-learning
6.37 score 64 stars 91 scripts 560 downloadscodebreak - Label Data Using a YAML Codebook
A light-weight framework for labeling coded data using a codebook saved as YAML text file.
Last updated 6 months ago
codebookdata-dictionary
2.48 score 6 stars 1 scriptssimul - Fast Simultaneous Confidence Bands Based on the Efficient Influence Function and Multiplier Bootstrap
Compute critical values for constructing uniform (simultaneous) confidence bands. The critical value is calculated using a multiplier bootstrap of the empirical efficient influence function as described by Kennedy (2019) <doi:10.1080/01621459.2017.1422737>. The multiplier bootstrap does not require resampling of the data but only simulation of the multipliers and is thus computationally efficient.
Last updated 4 years ago
bootstrapconfidence-intervalsnon-parametric-statisticscpp
2.40 score 5 stars 3 scripts 19 downloads