causal.estimate: Estimate causal effects using FLEXOR or other methods

In WMAP: Weighted Meta-Analysis with Pseudo-Populations

Estimate causal effects using FLEXOR or other methods

Description

This function estimates causal effects based on the specified pseudo-population method. The FLEXOR method involves an iterative two-step procedure.

Usage

causal.estimate(
  S,
  Z,
  X,
  Y,
  B = 100,
  method,
  naturalGroupProp = NULL,
  num.random = 40,
  gammaMin = 0.001,
  gammaMax = (1 - 0.001),
  seed = NULL,
  verbose = TRUE
)

Arguments

S	Vector of factor levels representing the study memberships. Takes values in {1, ..., J}.
Z	Vector of factor levels representing the group memberships. Takes values in {1, ..., K}.
X	Covariate matrix of N rows and p columns.
Y	Matrix of L outcomes, with dimensions N \times L.
B	Number of bootstrap samples for variance estimation. Default is 100.
method	Pseudo-population method, i.e., weighting method. Take values in FLEXOR, IC, or IGO.
naturalGroupProp	Relevant only for FLEXOR method: a fixed user-specified probability vector \theta.
num.random	Relevant only for FLEXOR method: number of random starting points of \gamma in the two-step iterative procedure. Default is 40.
gammaMin	Relevant only for FLEXOR method: Lower bound for each \gamma_s in the two-step iterative procedure. Default is 0.001.
gammaMax	Relevant only for FLEXOR method: Upper bound for each \gamma_s in the two-step iterative procedure. Default is 0.999.
seed	Seed for random number generation. Default is NULL.
verbose	Logical; if TRUE (default), displays progress messages during computation to the console. Set to FALSE to suppress these messages.

Value

An S3 list object with the following components:

percentESS

Percentage sample effective sample size (ESS) of the pseudo-population.

moments.ar

An array of dimension 3 \times K \times L, containing:

• Estimated means, standard deviations (SDs), and medians (dimension 1),

• For K groups (dimension 2),

• And L counterfactual outcomes (dimension 3).

otherFeatures.v

Estimated mean group differences for L outcomes.

collatedMoments.ar

An array of dimension 3 \times K \times L \times B, containing:

• moments.ar of the bth bootstrap sample (dimensions 1–3),

• For B bootstrap samples (dimension 4).

collatedOtherFeatures.mt

A matrix of dimension L \times B containing:

• otherFeatures.v of the bth bootstrap sample (dimension 1),

• For B bootstrap samples (dimension 2).

collatedESS

A vector of length B

containing percentage sample ESS for B bootstrap samples.

method

Pseudo-population method, i.e., weighting method.

Examples

data(demo)
set.seed(1)
causal.estimate(S, Z, X, Y, B = 5, method = "IC", naturalGroupProp)

WMAP documentation built on April 3, 2025, 8:55 p.m.