Description Usage Arguments Value References Examples

`ivlate`

is used to estimate the mean outcome among compliers (i.e., those encouraged by the instrument) had all subjects received treatment versus control.

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`y` |
outcome of interest. |

`a` |
binary treatment. |

`z` |
binary instrument. |

`x` |
covariate matrix. |

`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. |

`sl.lib` |
algorithm library for SuperLearner. Default library includes "earth", "gam", "glm", "glmnet", "glm.interaction", "mean", "ranger", "rpart". |

`project01` |
should the estimated compliance score be projected to space respecting 0-1 bounds and monotonicity? |

A list containing the following components:

`res` |
estimates/SEs/CIs/p-values for local average treatment effect E(Y(a=1)-Y(a=0)|A(z=1)>A(z=0)), as well as IV strength and sharpness. |

`nuis` |
subject-specific estimates of nuisance functions (i.e., IV propensity score and treatment/outcome regressions) |

`ifvals` |
matrix of estimated influence function values. |

(Also see references for function `ate`

)

Angrist JD, Imbens GW, Rubin DB (1996). Identification of causal effects using instrumental variables. *Journal of the American Statistical Association*.

Abadie A (2003). Semiparametric instrumental variable estimation of treatment response models. *Journal of Econometrics*.

Kennedy EH, Balakrishnan S, G'Sell M (2017). Complier classification with sharp instrumental variables. *Working Paper*.

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