WeightCreators: Generate Direct Adjusted Weights for Treatment Effect...

In propertee: Standardization-Based Effect Estimation with Optional Prior Covariance Adjustment

Generate Direct Adjusted Weights for Treatment Effect Estimation

Description

These should primarily be used inside models. See Details.

Usage

ett(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

att(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

ate(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

etc(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

atc(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

ato(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

olw(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

owt(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

pwt(specification = NULL, dichotomy = NULL, by = NULL, data = NULL)

Arguments

specification	optional; a StudySpecification object created by one of rct_spec(), rd_spec(), or obs_spec().
dichotomy	optional; a formula defining the dichotomy of the treatment variable if it isn't already 0/1. See details for more information. If ett() or ate() is called within a lmitt() call that specifies a dichotomy argument, that dichotomy will be used if the argument here has not been specified.
by	optional; named vector or list connecting names of unit of assignment/ variables in specification to unit of assignment/unitid/cluster variables in data. Names represent variables in the StudySpecification; values represent variables in the data. Only needed if variable names differ.
data	optional; the data for the analysis to be performed on. May be excluded if these functions are included as the weights argument of a model.

Details

These functions should primarily be used in the weight argument of lmitt() orlm(). All arguments are optional if used within those functions. If used on their own, specification and data must be provided.

• ate - Average treatment effect. Aliases: ate().

• ett - Effect of treatment on the treated. Aliases: ett(), att().

• etc - Effect of treatment on controls. Aliases: etc(), atc().

• ato - Overlap-weighted average effect. Aliases: ato(), olw, owt, pwt.

In a StudySpecification with blocks, the weights are generated as a function of the ratio of the number of treated units in a block versus the total number of units in a block.

In any blocks where that ratio is 0 or 1 (that is, all units in the block have the same treatment status), the weights will be 0. In effect this removes from the target population any block in which there is no basis for estimating either means under treatment or means under control.

If block is missing for a given observation, a weight of 0 is applied.

A dichotomy is specified by a formula consisting of a conditional statement on both the left-hand side (identifying treatment levels associated with "treatment") and the right hand side (identifying treatment levels associated with "control"). For example, if your treatment variable was called dose and doses above 250 are considered treatment, you might write:

ate(..., dichotomy = dose > 250 ~ dose <= 250

The period (.) can be used to assign all other units of assignment. For example, we could have written the same treatment regime as either

etc(..., dichotomy = dose > 250 ~ .

or

olw(..., dichotomy = . ~ dose <= 250

The dichotomy formula supports Relational Operators (see Comparison), Logical Operators (see Logic), and %in% (see match()).

The conditionals need not assign all values of treatment to control or treatment, for example, dose > 300 ~ dose < 200 does not assign 200 <= dose <= 300 to either treatment or control. This would be equivalent to manually generating a binary variable with NA whenever dose is between 200 and 300. Standard errors will reflect the sizes of the comparison groups specified by the dichotomy.

Tim Lycurgus contributed code for the computation of weights. The ‘overlap weight’ concept is due to Li, Morgan and Zaslavsky (2018), although the current implementation differs from that discussed in their paper in that it avoids estimated propensity scores.

Value

a WeightedStudySpecification object, which is a vector of numeric weights

References

Li, Fan, Kari Lock Morgan, and Alan M. Zaslavsky. "Balancing covariates via propensity score weighting." Journal of the American Statistical Association 113, no. 521 (2018): 390-400.

Examples

data(simdata)
spec <- rct_spec(z ~ unit_of_assignment(uoa1, uoa2), data = simdata)
summary(lmitt(y ~ 1, data = simdata, specification = spec, weights = ate()), vcov.type = "CR0")

propertee documentation built on Aug. 22, 2025, 1:09 a.m.