Description Usage Arguments Value References Examples

`ate`

is used to estimate the mean outcome in a population had
all subjects received given levels of a discrete (unconfounded) treatment, using
doubly robust methods with ensembled nuisance estimation.

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`y` |
outcome of interest. |

`a` |
discrete treatment. |

`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. |

A list containing the following components:

`res` |
estimates/SEs/CIs/p-values for population means and relevant contrasts. |

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

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

Robins JM, Rotnitzky A (1995). Semiparametric efficiency in multivariate regression models with missing data. *Journal of the American Statistical Association*.

Hahn J (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. *Econometrica*.

van der Laan MJ, Robins JM (2003). *Unified Methods for Censored Longitudinal Data and Causality* (Springer).

Tsiatis AA (2006). *Semiparametric Theory and Missing Data* (Springer).

Robins JM, Li L, Tchetgen Tchetgen ET, van der Vaart A (2008). Higher order influence functions and minimax estimation of nonlinear functionals. *Probability and Statistics: Essays in Honor of David A. Freedman*.

Zheng W, van der Laan (2010). Asymptotic theory for cross-validated targeted maximum likelihood estimation *UC Berkeley Division of Biostatistics Working Paper Series*.

Chernozhukov V, Chetverikov V, Demirer M, et al (2016). Double machine learning for treatment and causal parameters.

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