Deciding Which Estimator to Use
Which model you should use depends on what you are trying to model and the type of data you have. The table below can serve as a useful reference when deciding which model to use for a given dataset and causal question.
| Model | Struct | Causal Estimands | Supported Treatment Types | Supported Outcome Types |
|---|---|---|---|---|
| Interrupted Time Series Analysis | InterruptedTimeSeries | ATE, Cumulative Treatment Effect | Binary | Continuous, Count[1], Time to Event |
| G-computation | GComputation | ATE, ATT, ITT | Binary | Binary, Continuous, Time to Event, Count[1] |
| Double Machine Learning | DoubleMachineLearning | ATE | Binary, Count[1], Continuous | Binary, Count[1], Continuous, Time to Event |
| S-learning | SLearner | CATE | Binary | Binary, Continuous, Time to Event, Count[1] |
| T-learning | TLearner | CATE | Binary | Binary, Continuous, Count[1], Time to Event |
| X-learning | XLearner | CATE | Binary | Binary, Continuous, Count[1], Time to Event |
| R-learning | RLearner | CATE | Binary, Count[1], Continuous | Binary, Count[1], Continuous, Time to Event |
| Doubly Robust Estimation | DoublyRobustLearner | CATE | Binary | Binary, Continuous, Count[1], Time to Event |
- 1Similar to other packages, predictions of count variables is treated as a continuous regression task.