Fit a probability of censoring model, e.g. glm(censoring ~ predictors, family = binomial())

Create weights using inverse probability strategy

Use weights in your causal model

We won’t do it here, but you can include many types of weights in a given model. Just take their product, e.g. multiply inverse propensity of treatment weights by inverse propensity of censoring weights.

Your Turn

Work through Your Turns 1-3 in 13-bonus-selection-bias.qmd