Introduction | Learner Template | Defining your Learner | Learner Name and Class | public$initialize | public$special_functions | private$.properties | private$.required_packages | User Interface for Learners | private$.train | private$.predict | private$.chain | Advanced: Learners with sub-learners | Documenting and Testing your Learner | Submitting your Learner to sl3

Background and Motivations | Problem Setup and Notation | Interventional (In)Direct Effects | Efficient Estimation | Data Analysis Example | Setting up the data example | Ensemble learning of nuisance functions | Estimating the direct effect | Estimating the indirect effect | References

Background | Decomposing the population intervention effect | Estimating the effect decomposition term | Estimating the direct effect | References

Introduction | Motivating Example | NPSEM | Causal Considerations | Example | tmle3_Task | Likelihood | Likelihood Factor Objects | LF_emp | LF_fit | Specifying a priori knowledge. | Counterfactual Likelihoods | LF_static | Other intervention likelihood factor types | Counterfactual Tasks | Update Procedure | Target Parameter | tmle3_Fit - Putting it all together | TMLE Specification | Conclusion

Introduction | Data and Notation | Simulate Data | Methodology | Interlude: Constructing Optimal Stacked Regressions with sl3 | Targeted Estimation of Stochastic Interventions Effects | Statistical Inference for Targeted Maximum Likelihood Estimates | References

Introduction | Data and Notation | Simulate Data | Methodology | Defining a grid of counterfactual interventions | Interlude: Constructing Optimal Stacked Regressions with sl3 | Initializing vimshift through its tmle3_Spec | Targeted Estimation of Stochastic Interventions Effects | Inference with Marginal Structural Models | Directly Targeting the MSM Parameter $\beta$ | References

Introduction | Example Data | Basics of Object Oriented Programming | sl3 objects | Tasks | Learners | Finding Learners | Learner Parameters | Composing Learners | Pipelines | Stacks | Cross-validation | The Super Learner Algorithm | Computation with delayed | Session Information | References