ATE_internal: Estimating the Average Treatment Effect (ATE) in an internal...

In CausalMetaR: Causally Interpretable Meta-Analysis

Estimating the Average Treatment Effect (ATE) in an internal target population using multi-source data

Description

Doubly-robust and efficient estimator for the ATE in each internal target population using multi-source data.

Usage

ATE_internal(
  X,
  Y,
  S,
  A,
  cross_fitting = FALSE,
  replications = 10L,
  source_model = "MN.glmnet",
  source_model_args = list(),
  treatment_model_type = "separate",
  treatment_model_args = list(),
  outcome_model_args = list(),
  show_progress = TRUE
)

Arguments

X	Data frame (or matrix) containing the covariate data in the multi-source data. It should have n rows and p columns. Character variables will be converted to factors.
Y	Vector of length n containing the outcome.
S	Vector of length n containing the source indicator. If S is a factor, it will maintain its level order; otherwise it will be converted to a factor with the default level order. The order will be carried over to the outputs and plots.
A	Vector of length n containing the binary treatment (1 for treated and 0 for untreated).
cross_fitting	Logical specifying whether sample splitting and cross fitting should be used.
replications	Integer specifying the number of sample splitting and cross fitting replications to perform, if cross_fitting = TRUE. The default is 10L.
source_model	Character string specifying the (penalized) multinomial logistic regression for estimating the source model. It has two options: "MN.glmnet" (default) and "MN.nnet", which use glmnet and nnet respectively.
source_model_args	List specifying the arguments for the source model (in glmnet or nnet).
treatment_model_type	Character string specifying how the treatment model is estimated. Options include "separate" (default) and "joint". If "separate", the treatment model (i.e., P(A=1|X, S=s)) is estimated by regressing A on X within each specific internal population S=s. If "joint", the treatment model is estimated by regressing A on X and S using the multi-source population.
treatment_model_args	List specifying the arguments for the treatment model (in SuperLearner).
outcome_model_args	List specifying the arguments for the outcome model (in SuperLearner).
show_progress	Logical specifying whether to print a progress bar for the cross-fit replicates completed, if cross_fitting = TRUE.

Details

Data structure:

The multi-source dataset consists the outcome Y, source S, treatment A, and covariates X (n \times p) in the internal populations. The data sources can be trials, observational studies, or a combination of both.

Estimation of nuissance parameters:

The following models are fit:

• Propensity score model: \eta_a(X)=P(A=a|X). We perform the decomposition P(A=a|X)=\sum_{s} P(A=a|X, S=s)P(S=s|X) and estimate P(A=1|X, S=s) (i.e., the treatment model) and q_s(X)=P(S=s|X) (i.e., the source model).

• Outcome model: \mu_a(X)=E(Y|X, A=a)

The models are estimated by SuperLearner with the exception of the source model which is estimated by glmnet or nnet.

ATE estimation:

The ATE estimator is

\dfrac{\widehat \kappa}{n}\sum\limits_{i=1}^{n} \Bigg[ I(S_i = s) \widehat \mu_a(X_i) +I(A_i = a) \dfrac{\widehat q_{s}(X_i)}{\widehat \eta_a(X_i)} \Big\{ Y_i - \widehat \mu_a(X_i) \Big\} \Bigg],

where \widehat \kappa=\{n^{-1} \sum_{i=1}^n I(S_i=s)\}^{-1}. The estimator is doubly robust and non-parametrically efficient.

To achieve non-parametric efficiency and asymptotic normality, it requires that ||\widehat \mu_a(X) -\mu_a(X)||\big\{||\widehat \eta_a(X) -\eta_a(X)||+||\widehat q_s(X) -q_s(X)||\big\}=o_p(n^{-1/2}). In addition, sample splitting and cross-fitting can be performed to avoid the Donsker class assumption.

When a data source is a randomized trial, it is still recommended to estimate the propensity score for optimal efficiency.

Value

An object of class "ATE_internal". This object is a list with the following elements:

df_dif	A data frame containing the treatment effect (mean difference) estimates for the internal populations.
df_A0	A data frame containing the potential outcome mean estimates under A = 0 for the internal populations.
df_A1	A data frame containing the potential outcome mean estimates under A = 1 for the internal populations.
fit_outcome	Fitted outcome model.
fit_source	Fitted source model.
fit_treatment	Fitted treatment model(s).

References

Robertson, S.E., Steingrimsson, J.A., Joyce, N.R., Stuart, E.A. and Dahabreh, I.J. (2021). Center-specific causal inference with multicenter trials: Reinterpreting trial evidence in the context of each participating center. arXiv preprint arXiv:2104.05905.

Wang, G., McGrath, S., and Lian, Y. (In press) CausalMetaR: An R package for performing causally interpretable meta-analyses, Research Synthesis Methods.

Examples

ai <- ATE_internal(
  X = dat_multisource[, 1:10],
  Y = dat_multisource$Y,
  S = dat_multisource$S,
  A = dat_multisource$A,
  source_model = "MN.glmnet",
  source_model_args = list(),
  treatment_model_type = "separate",
  treatment_model_args = list(
    family = binomial(),
    SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"),
    cvControl = list(V = 5L)
  ),
  outcome_model_args = list(
    family = gaussian(),
    SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"),
    cvControl = list(V = 5L)
  )
)

CausalMetaR documentation built on April 12, 2025, 2:06 a.m.