• lmtp R package by Nick Williams implementing the methods we proposed here. Some features are:​​​

    • Estimation of causal effects for static interventions and modified treatment policies in longitudinal data​

    • Continuous, categorical, and binary exposures

    • Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters

    • Right-censored data

  • adjrct R package by Nick Williams implementing the methods we proposed here and in references therein. Some features are:​​​

    • Estimation of covariate adjusted effects for randomized trials, with efficiency guarantees

    • Estimation of effects for ordinal and time-to-event endpoints

    • Incorporation of machine learning for maximum efficiency gains

  • medshift R package by Nima Hejazi:

    • ​Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters

    • Mediation analysis for stochastic interventions

    • Continuous exposures

    • Multivariate mediators

  • medoutcon R package by Nima Hejazi:

    • ​Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters

    • Mediation analysis for binary exposures in the presence of intermediate confounders

    • Multivariate mediators