Description Usage Arguments Details Value References Examples

This function implements model-assisted inference for average treatment effects, using non-regularized calibrated estimation.

1 |

`y` |
An |

`tr` |
An |

`x` |
An |

`ploss` |
A loss function used in propensity score estimation (either "ml" or "cal"). |

`yloss` |
A loss function used in outcome regression (either "gaus" for continuous outcomes or "ml" for binary outcomes). |

`off` |
A |

For calibrated estimation, two sets of propensity scores are separately estimated for the untreated and treated as discussed in Tan (2020a, 2020b).
See also **Details** for `mn.nreg`

.

`ps` |
A list containing the results from fitting the propensity score model by |

`mfp` |
An |

`or` |
A list containing the results from fitting the outcome regression model by |

`mfo` |
An |

`est` |
A list containing the results from augmented IPW estimation by |

Tan, Z. (2020a) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data, *Biometrika*, 107, 137<e2><80><93>158.

Tan, Z. (2020b) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, *Annals of Statistics*, 48, 811<e2><80><93>837.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
data(simu.data)
n <- dim(simu.data)[1]
p <- dim(simu.data)[2]-2
y <- simu.data[,1]
tr <- simu.data[,2]
x <- simu.data[,2+1:p]
x <- scale(x)
# include only 10 covariates
x2 <- x[,1:10]
ate.cal <- ate.nreg(y, tr, x2, ploss="cal", yloss="gaus")
matrix(unlist(ate.cal$est), ncol=2, byrow=TRUE,
dimnames=list(c("one", "ipw", "or", "est", "var", "ze",
"diff.est", "diff.var", "diff.ze"), c("untreated", "treated")))
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.