CBPS.fit {CBPS} | R Documentation |

## CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data

### Description

CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data

### Usage

```
CBPS.fit(
treat,
X,
baselineX,
diffX,
ATT,
method,
iterations,
standardize,
twostep,
sample.weights = sample.weights,
...
)
```

### Arguments

`treat` |
A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric. |

`X` |
A covariate matrix. |

`baselineX` |
Similar to |

`diffX` |
Similar to |

`ATT` |
Default is 1, which finds the average treatment effect on the treated interpreting the second level of the treatment factor as the treatment. Set to 2 to find the ATT interpreting the first level of the treatment factor as the treatment. Set to 0 to find the average treatment effect. For non-binary treatments, only the ATE is available. |

`method` |
Choose "over" to fit an over-identified model that combines the propensity score and covariate balancing conditions; choose "exact" to fit a model that only contains the covariate balancing conditions. |

`iterations` |
An optional parameter for the maximum number of iterations for the optimization. Default is 1000. |

`standardize` |
Default is |

`twostep` |
Default is |

`sample.weights` |
Survey sampling weights for the observations, if applicable. When left NULL, defaults to a sampling weight of 1 for each observation. |

`...` |
Other parameters to be passed through to |

### Value

CBPS.fit object

*CBPS*version 0.23 Index]