AIPW_nuis {AIPW} | R Documentation |

`AIPW_nuis`

class for users to manually input nuisance functions (estimates from the exposure and the outcome models)

Create an AIPW_nuis object that uses users' input nuisance functions from the exposure model *P(A| W)*,
and the outcome models *P(Y| do(A=0), W)* and *P(Y| do(A=1), W.Q)*:

*
ψ(a) = E{[ I(A=a) / P(A=a|W) ] * [Y-P(Y=1|A,W)] + P(Y=1| do(A=a),W) }
*

Note: If outcome is missing, replace (A=a) with (A=a, observed=1) when estimating the propensity scores.

`AIPW_nuis`

object

`AIPW$new(Y = NULL, A = NULL, tmle_fit = NULL, verbose = TRUE)`

Argument | Type | Details |

`Y` | Integer | A vector of outcome (binary (0, 1) or continuous) |

`A` | Integer | A vector of binary exposure (0 or 1) |

`mu0` | Numeric | User input of P(Y=1| do(A = 0),W_Q) |

`mu1` | Numeric | User input of P(Y=1| do(A = 1),W_Q) |

`raw_p_score` | Numeric | User input of P(A=a|W_g) |

`verbose` | Logical | Whether to print the result (Default = TRUE) |

`stratified_fitted` | Logical | Whether mu0 & mu1 was estimated only using `A=0` & `A=1` (Default = FALSE) |

Methods | Details | Link |

`summary()` | Summary of the average treatment effects from AIPW | summary.AIPW_base |

`plot.p_score()` | Plot the propensity scores by exposure status | plot.p_score |

`plot.ip_weights()` | Plot the inverse probability weights using truncated propensity scores | plot.ip_weights |

Variable | Generated by | Return |

`n` | Constructor | Number of observations |

`obs_est` | Constructor | Components calculating average causal effects |

`estimates` | `summary()` | A list of Risk difference, risk ratio, odds ratio |

`result` | `summary()` | A matrix contains RD, ATT, ATC, RR and OR with their SE and 95%CI |

`g.plot` | `plot.p_score()` | A density plot of propensity scores by exposure status |

`ip_weights.plot` | `plot.ip_weights()` | A box plot of inverse probability weights |

`stratified_fit`

An indicator for whether the outcome model is fitted stratified by exposure status in the

`fit()`

method. Only when using`stratified_fit()`

to turn on`stratified_fit = TRUE`

,`summary`

outputs average treatment effects among the treated and the controls.`obs_est`

This list includes propensity scores (

`p_score`

), counterfactual predictions (`mu`

,`mu1`

&`mu0`

) and efficient influence functions (`aipw_eif1`

&`aipw_eif0`

)`g.plot`

This plot is generated by

`ggplot2::geom_density`

`ip_weights.plot`

This plot uses truncated propensity scores stratified by exposure status (

`ggplot2::geom_boxplot`

)

[Package *AIPW* version 0.6.3.2 Index]