Description Details Value Constructor Public Methods Public Variables Examples

`AIPW_tmle`

class uses a fitted `tmle`

or `tmle3`

object as input

Create an AIPW_tmle object that uses the estimated efficient influence function from a fitted `tmle`

or `tmle3`

object

`AIPW_tmle`

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) |

`tmle_fit` | Object | A fitted `tmle` or `tmle3` object |

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

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 |

`obs_est`

This list extracts from the fitted

`tmle`

object. It 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`

)

1 2 3 4 5 6 7 8 9 10 11 12 | ```
vec <- function() sample(0:1,100,replace = TRUE)
df <- data.frame(replicate(4,vec()))
names(df) <- c("A","Y","W1","W2")
## From tmle
library(tmle)
library(SuperLearner)
tmle_fit <- tmle(Y=df$Y,A=df$A,W=subset(df,select=c("W1","W2")),
Q.SL.library="SL.glm",
g.SL.library="SL.glm",
family="binomial")
AIPW_tmle$new(A=df$A,Y=df$Y,tmle_fit = tmle_fit,verbose = TRUE)$summary()
``` |

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