Description Usage Arguments Value Details References Examples

`ipsi`

is used to estimate effects of incremental propensity score interventions, i.e., estimates of mean outcomes if the odds of receiving treatment were multiplied by a factor delta.

1 | ```
ipsi(dat, x.trt, x.out, delta.seq, nsplits)
``` |

`y` |
outcome of interest measured at end of study. |

`a` |
binary treatment. |

`x.trt` |
covariate matrix for treatment regression. |

`x.out` |
covariate matrix for outcome regression. |

`time` |
measurement time. |

`id` |
subject identifier. |

`delta.seq` |
sequence of delta increment values. |

`nsplits` |
integer number of sample splits for nuisance estimation. If nsplits=1, sample splitting is not used, and nuisance functions are estimated on full sample (in which case validity of SEs/CIs requires empirical process conditions). Otherwise must have nsplits>1. |

A list containing the following components:

`res` |
estimates/SEs and uniform CIs for population means. |

`res.ptwise` |
estimates/SEs and pointwise CIs for population means. |

`calpha` |
multiplier bootstrap critical value. |

Treatment and covariates are expected to be time-varying and measured throughout the course of the study. Therefore if `n`

is the number of subjects and `T`

the number of timepoints, then `a`

, `time`

, and `id`

should all be vectors of length `n`

x`T`

, and `x.trt`

and `x.out`

should be matrices with `n`

x`T`

rows. However `y`

should be a vector of length `n`

since it is only measured at the end of the study. The subject ordering should be consistent across function inputs, based on the ordering specified by `id`

. See example below for an illustration.

Kennedy EH. Nonparametric causal effects based on incremental propensity score interventions. arxiv:1704.00211

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