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.
AIPW object
AIPW$new(Y = NULL, A = NULL, W = NULL, W.Q = NULL, W.g = NULL, Q.SL.library = NULL, g.SL.library = NULL, k_split = 10, verbose = TRUE, save.sl.fit = FALSE)
| Argument | Type | Details |
Y | Integer | A vector of outcome (binary (0, 1) or continuous) |
A | Integer | A vector of binary exposure (0 or 1) |
W | Data | Covariates for both exposure and outcome models. |
W.Q | Data | Covariates for the outcome model (Q). |
W.g | Data | Covariates for the exposure model (g). |
Q.SL.library | SL.library | Algorithms used for the outcome model (Q). |
g.SL.library | SL.library | Algorithms used for the exposure model (g). |
k_split | Integer | Number of folds for splitting (Default = 10). |
verbose | Logical | Whether to print the result (Default = TRUE) |
save.sl.fit | Logical | Whether to save Q.fit and g.fit (Default = FALSE) |
W, W.Q & W.gIt can be a vector, matrix or data.frame. If and only if W == NULL, W would be replaced by W.Q and W.g.
Q.SL.library & g.SL.libraryMachine learning algorithms from SuperLearner libraries
k_splitIt ranges from 1 to number of observation-1.
If k_split=1, no cross-fitting; if k_split>=2, cross-fitting is used
(e.g., k_split=10, use 9/10 of the data to estimate and the remaining 1/10 leftover to predict).
NOTE: it's recommended to use cross-fitting.
save.sl.fitThis option allows users to save the fitted sl object (libs$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_fitAn 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_estAfter 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.plotThis plot is generated by ggplot2::geom_density
ip_weights.plotThis plot uses truncated propensity scores stratified by exposure status (ggplot2::geom_boxplot)
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.
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.
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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