1. Data structure | 2. Quick overview: Functionality | 3. Detailed procedures: Customize your call | Step 1. Define the intervention | Step 2. Make causal assumptions for identification | Step 3. Specify a treatment model and/or an outcome model | Choose an estimation algorithm | Why doubly robust? A side note | Why sample splitting? Another side note | Produce standard errors | 4. Estimate by machine learning: Two examples | Estimation with Generalized Additive Models (GAMs) via mgcv | Estimation with random forests via ranger | Estimates from these three algorithms are roughly the same | A warning about complex estimators | 5. Estimate with stochastic assignment rules | 6. Conclusion | References