AIPW_nuis: Augmented Inverse Probability Weighting (AIPW) uses tmle or...
In AIPW: Augmented Inverse Probability Weighting

Description Details Value Constructor Public Methods Public Variables

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)

Constructor Arguments

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

Public Variable Details

stratified_fit: An indicator for whether the outcome model is fitted stratified by exposure status in thefit() 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)