Description Details Value Constructor Public Methods Public Variables References Examples

An R6Class of AIPW for estimating the average causal effects with users' inputs of exposure, outcome, covariates and related libraries for estimating the efficient influence function.

An AIPW object is constructed by `new()`

with users' inputs of data and causal structures, then it `fit()`

the data using the
libraries in `Q.SL.library`

and `g.SL.library`

with `k_split`

cross-fitting, and provides results via the `summary()`

method.
After using `fit()`

and/or `summary()`

methods, propensity scores and inverse probability weights by exposure status can be
examined with `plot.p_score()`

and `plot.ip_weights()`

, respectively.

If outcome is missing, analysis assumes missing at random (MAR) by estimating propensity scores of I(A=a, observed=1) with all covariates `W`

.
(`W.Q`

and `W.g`

are disabled.) Missing exposure is not supported.

See examples for illustration.

`AIPW`

object

`AIPW$new(Y = NULL, A = NULL, W = NULL, W.Q = NULL, W.g = NULL, Q.SL.library = NULL, g.SL.library = NULL, k_split = 10, verbose = TRUE, save.sl.fit = FALSE)`

Argument | Type | Details |

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

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

`W` | Data | Covariates for both exposure and outcome models. |

`W.Q` | Data | Covariates for the outcome model (Q). |

`W.g` | Data | Covariates for the exposure model (g). |

`Q.SL.library` | SL.library | Algorithms used for the outcome model (Q). |

`g.SL.library` | SL.library | Algorithms used for the exposure model (g). |

`k_split` | Integer | Number of folds for splitting (Default = 10). |

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

`save.sl.fit` | Logical | Whether to save Q.fit and g.fit (Default = FALSE) |

`W`

,`W.Q`

&`W.g`

It can be a vector, matrix or data.frame. If and only if

`W == NULL`

,`W`

would be replaced by`W.Q`

and`W.g`

.`Q.SL.library`

&`g.SL.library`

Machine learning algorithms from SuperLearner libraries

`k_split`

It ranges from 1 to number of observation-1. If k_split=1, no cross-fitting; if k_split>=2, cross-fitting is used (e.g.,

`k_split=10`

, use 9/10 of the data to estimate and the remaining 1/10 leftover to predict).**NOTE: it's recommended to use cross-fitting.**`save.sl.fit`

This option allows users to save the fitted sl object (libs$Q.fit & libs$g.fit) for debug use.

**Warning: Saving the SuperLearner fitted object may cause a substantive storage/memory use.**

Methods | Details | Link |

`fit()` | Fit the data to the AIPW object | fit.AIPW |

`stratified_fit()` | Fit the data to the AIPW object stratified by `A` | stratified_fit.AIPW |

`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 |

`stratified_fitted` | `stratified_fit()` | Fit the outcome model stratified by exposure status |

`obs_est` | `fit()` & `summary()` | 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 |

`libs` | `fit()` | SuperLearner libraries and their fitted objects |

`sl.fit` | Constructor | A wrapper function for fitting SuperLearner |

`sl.predict` | Constructor | A wrapper function using `sl.fit` to predict |

`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`

After using

`fit()`

and`summary()`

methods, this list contains the propensity scores (`p_score`

), counterfactual predictions (`mu`

,`mu1`

&`mu0`

) and efficient influence functions (`aipw_eif1`

&`aipw_eif0`

) for later average treatment effect calculations.`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`

)

Zhong Y, Kennedy EH, Bodnar LM, Naimi AI (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. *American Journal of Epidemiology*.

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

Chernozhukov V, Chetverikov V, Demirer M, et al (2018). Double/debiased machine learning for treatment and structural parameters. *The Econometrics Journal*.

Kennedy EH, Sjolander A, Small DS (2015). Semiparametric causal inference in matched cohort studies. *Biometrika*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(SuperLearner)
library(ggplot2)
#create an object
aipw_sl <- AIPW$new(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
Q.SL.library="SL.mean",g.SL.library="SL.mean",
k_split=1,verbose=FALSE)
#fit the object
aipw_sl$fit()
# or use `aipw_sl$stratified_fit()` to estimate ATE and ATT/ATC
#calculate the results
aipw_sl$summary(g.bound = 0.025)
#check the propensity scores by exposure status after truncation
aipw_sl$plot.p_score()
``` |

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