genTarget: Generating target MTR moments
In ivmte: Instrumental Variables: Extrapolation by Marginal Treatment Effects

Description Usage Arguments Value Examples

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

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
)

`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.

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.

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)