genTarget {ivmte}

Generating target MTR moments

Description

This function estimates the moment of each MTR term under the target weight.

Usage

genTarget(
  treat,
  m0,
  m1,
  target,
  target.weight0,
  target.weight1,
  target.knots0,
  target.knots1,
  late.Z,
  late.from,
  late.to,
  late.X,
  eval.X,
  genlate.lb,
  genlate.ub,
  data,
  splinesobj,
  pmodobj,
  pm0,
  pm1,
  noisy = TRUE
)

Arguments

treat

variable name for treatment indicator. The name can be provided with or without quotation marks.

m0

one-sided formula for the marginal treatment response function for the control group. Splines may also be incorporated using the expression uSpline, e.g. uSpline(degree = 2, knots = c(0.4, 0.8), intercept = TRUE). The intercept argument may be omitted, and is set to TRUE by default.

m1

one-sided formula for the marginal treatment response function for the treated group. See m0 for details.

target

character, target parameter to be estimated. The function allows for ATE ('ate'), ATT ('att'), ATU ('atu'), LATE ('late'), and generalized LATE ('genlate').

target.weight0

user-defined weight function for the control group defining the target parameter. A list of functions can be submitted if the weighting function is in fact a spline. The arguments of the function should be variable names in data. If the weight is constant across all observations, then the user can instead submit the value of the weight instead of a function.

target.weight1

user-defined weight function for the treated group defining the target parameter. See target.weight0 for details.

target.knots0

user-defined set of functions defining the knots associated with spline weights for the control group. The arguments of the function should consist only of variable names in data. If the knots are constant across all observations, then the user can instead submit the vector of knots instead of a function.

target.knots1

user-defined set of functions defining the knots associated with spline weights for the treated group. See target.knots0 for details.

late.Z

vector of variable names used to define the LATE.

late.from

baseline set of values of Z used to define the LATE.

late.to

comparison set of values of Z used to define the LATE.

late.X

vector of variable names of covariates to condition on when defining the LATE.

eval.X

numeric vector of the values to condition variables in late.X on when estimating the LATE.

genlate.lb

lower bound value of unobservable u for estimating the generalized LATE.

genlate.ub

upper bound value of unobservable u for estimating the generalized LATE.

data

data.frame or data.table used to estimate the treatment effects.

splinesobj

list of spline components in the MTRs for treated and control groups. Spline terms are extracted using removeSplines. This object is supposed to be a dictionary of splines, containing the original calls of each spline in the MTRs, their specifications, and the index used for naming each basis spline.

pmodobj

A vector of propensity scores.

pm0

A list of the monomials in the MTR for d = 0.

pm1

A list of the monomials in the MTR for d = 1.

noisy

boolean, default set to TRUE. If TRUE, then messages are provided throughout the estimation procedure. Set to FALSE to suppress all messages, e.g. when performing the bootstrap.

Value

A list containing either the vectors of gamma moments for D = 0 and D = 1, or a matrix of individual gamma values for D = 0 and D = 1. Additoinally, two vectors are returned. xindex0 and xindex1 list the variables that interact with the unobservable u in m0 and m1. uexporder0 and uexporder1 lists the exponents of the unobservable u in each term it appears in.

Examples

dtm <- ivmte:::gendistMosquito()

## Declare MTR functions
formula1 = ~ 1 + u
formula0 = ~ 1 + u
splinesList = list(removeSplines(formula0), removeSplines(formula1))

## Declare propensity score model
propensityObj <- propensity(formula = d ~ z,
                            data = dtm,
                            link = "linear")

## Construct MTR polynomials
polynomials0 <- polyparse(formula = formula0,
                 data = dtm,
                 uname = u,
                 as.function = FALSE)

polynomials1 <- polyparse(formula = formula0,
                 data = dtm,
                 uname = u,
                 as.function = FALSE)

## Generate target gamma moments
genTarget(treat = "d",
          m0 = ~ 1 + u,
          m1 = ~ 1 + u,
          target = "atu",
          data = dtm,
          splinesobj = splinesList,
          pmodobj = propensityObj,
          pm0 = polynomials0,
          pm1 = polynomials1)