GenericML (version 0.2.2)

setup_diff: Setup function for diff arguments

Description

This setup function controls how differences of generic target parameters are taken. Returns a list with two components, called subtract_from and subtracted. The first element (subtract_from) denotes what shall be the base group to subtract from in the generic targets of interest (GATES or CLAN); either "most" or "least". The second element (subtracted) are the groups to be subtracted from subtract_from, which is a subset of \({1,2,...,K}\), where \(K\) equals the number of groups. The number of groups should be consistent with the number of groups induced by the argument quantile_cutoffs, which is the cardinality of quantile_cutoffs, plus one.

Usage

setup_diff(subtract_from = "most", subtracted = 1)

Arguments

subtract_from

String indicating the base group to subtract from, either "most" (default) or "least". The most affected group corresponds to the \(K\)-th group in the paper (there are \(K\) groups). The least affected group corresponds to the first group.

subtracted

Vector indicating the groups to be subtracted from the group specified in subtract_from. If there are \(K\) groups, subtracted should be a subset of \({1,2,...,K}\). Be careful to not specify a zero difference: If subtract_from = "most", subtracting group K results in a zero difference. Same if subtract_from = "least" and we subtract group 1.

Value

An object of class "setup_diff", consisting of the following components:

subtract_from

A character equal to "most" or "least".

subtracted

A numeric vector of group indices.

See the description above for details.

Details

The output of this setup function is intended to be used as argument in the functions GenericML() and GenericML_single() (arguments diff_GATES, diff_CLAN), as well as GATES() and CLAN() (argument diff).

References

Chernozhukov V., Demirer M., Duflo E., Fern<U+00E1>ndez-Val I. (2020). “Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments.” arXiv preprint arXiv:1712.04802. URL: https://arxiv.org/abs/1712.04802.

See Also

GenericML(), GenericML_single(), CLAN(), GATES(), setup_X1(), setup_vcov()

Examples

# NOT RUN {
## specify quantile cutoffs (the 4 quartile groups here)
quantile_cutoffs <- c(0.25, 0.5, 0.75)

## Use group difference GK-G1 as generic targets in GATES and CLAN
## Gx is the x-th group
setup_diff(subtract_from = "most", subtracted = 1)

## Use GK-G1, GK-G2, GK-G3 as differenced generic targets
setup_diff(subtract_from = "most", subtracted = c(1,2,3))

## Use G1-G2, G1-G3 as differenced generic targets
setup_diff(subtract_from = "least", subtracted = c(3,2))

# }