Package: WeightIt 2.1.0.9001

Introduction | Identifying the estimand | G-computation | Modeling the Outcome | Estimating Standard Errors and Confidence Intervals | Asymptotically Correct Standard Errors Using M-estimation | Robust Standard Errors | Bootstrapping | Estimating Treatment Effects and Standard Errors After Weighting | The Standard Case: Binary Treatment with Asymptotically Correct SEs | Adjustments to the Standard Case | Weighting for the ATT or ATC | Weighting for estimands other than the ATT, ATC, or ATE | Binary, count, and categorical outcomes | Survival outcomes | Using sampling weights with complex surveys | Multi-Category Treatments | Continuous Treatments | Longitudinal Treatments | Moderation Analysis | Reporting Results | References | Code to Generate Data used in Examples

Introduction | Marginal Structural Models for Longitudinal Treatments | The Estimand | Time-Varying Confounding | Assumptions | Inverse Probability Weights | Estimating Weights for a Longitudinal Treatment | The Data | Initial Imbalance | Estimating the Weights | Assessing Balance | Estimating the Treatment Effect | Accounting for Loss to Follow-Up | The Estimand with Censoring | Simulating Loss to Follow-Up | Other Considerations | References

Propensity score weighting using GLMs (method = "glm") | missing = "saem" | Binary and multi-category treatments with link = "br.logit" | Multi-category treatments with multi.method = "mclogit" | Multilevel treatment models with random effects | Multi-category treatments with multi.method = "mnp" | Propensity score weighting using GBM (method = "gbm") | Covariate Balancing Propensity Score weighting (method = "cbps") | Nonparametric Covariate Balancing Propensity Score weighting (method = "npcbps") | Entropy balancing (method = "ebal") | Inverse probability tilting (method = "ipt") | Stable Balancing Weights (method = "optweight") | Propensity score weighting using SuperLearner (method = "super") | Propensity score weighting using BART (method = "bart") | Energy balancing (method = "energy") | Characteristic function distance balancing (method = "cfd")

Introduction | Balancing Weights for a Point Treatment | Balancing Weights for a Longitudinal Treatment | Accounting for censoring | References

Introduction | Weighting | Balance and Effective Sample Size | Estimands and Target Populations | Treatment Types | Parametric Methods | Propensity Score Weighting Using Generalized Linear Models (method = "glm") | Covariate Balancing Propensity Score Weighting (method = "cbps") | Inverse Probability Tilting (method = "ipt") | Machine Learning Methods | Propensity Score Weighting Using Generalized Boosted Models (method = "gbm") | Propensity Score Weighting Using SuperLearner (method = "super") | Propensity Score Weighting Using BART (method = "bart") | Optimization-Based Methods | Entropy Balancing (method = "ebal") | Stable Balancing Weights (method = "optweight") | Nonparametric Covariate Balancing Propensity Score Weighting (method = "npcbps") | Energy Balancing (method = "energy") | Characteristic Function Distance Balancing (method = "cfd") | Additional Options | Balancing Higher Moments, Interactions, and Quantiles (moments, int, quantile) | Stabilizing Weights (stabilize) | Trimming Weights (trim()) | Calibrating Propensity Scores (calibrate()) | Marginal Mean Weighting Through Stratification (subclass) | Supplying Propensity Scores (ps) | Choosing a Weighting Method | Summary of the Weighting Methods | A Workflow for Selecting a Method | Reporting the Weighting Specification | References