# 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 | Binary, Continuous |

G-computation | GComputation | ATE, ATT, ITT | Binary | Binary, Continuous, Time to Event |

Double Machine Learning | DoubleMachineLearning | ATE | Binary, Count, Categorical, Continuous | Continuous |

S-learning | SLearner | CATE | Binary | Binary, Continuous, Count |

T-learning | TLearner | CATE | Binary | Binary, Continuous |

X-learning | XLearner | CATE | Binary | Binary, Continuous, Count |

R-learning | RLearner | CATE | Binary, Count, Categorical, Continuous | Continuous |