point_estimator: Point estimator for gap-closing estimands

In gapclosing: Estimate Gaps Under an Intervention

Description

This is an internal function typically called from other functions rather than by the user. It uses a learning sample to learn the nuisance functions (treatment and outcome model) and then an auxiliary estimation sample to use those functions in estimation of the gap-closing estimand. For single-sample estimation, both the learning and estimation samples are the same. For cross-fitting, this function is called repeatedly with the roles of each dataset swapped.

Usage

point_estimator(
  data_learn,
  data_estimate,
  counterfactual_assignments,
  outcome_formula,
  treatment_formula,
  category_name,
  outcome_name,
  treatment_name,
  treatment_algorithm = "glm",
  outcome_algorithm = "lm",
  weight_name = NULL
)

Arguments

data_learn	Data frame in which treatment and outcome models will be learned
data_estimate	Data frame in which the learned models will be converted to an estimate of the gap-closing estimand
counterfactual_assignments	Numeric scalar or vector of length nrow(data), each element of which is on the [0,1] interval. If a scalar, the counterfactual probability by which all units are assigned to treatment condition 1. If a vector, each element i corresponds to the counterfactual probability by which each unit i is assigned to treatment condition 1.
outcome_formula	Model formula the outcome. Covariates should include those needed for causal identification of the treatment effect (e.g. as defended in your Directed Acyclic Graph). If outcome_algorithm = "ranger", then the outcome model will be fit separately on the treatment and control groups. Otherwise, the user must specify all interactions in the formula.
treatment_formula	Treatment formula, in the style formula(treatment ~ covariates). Covariates should include those needed for causal identification of the treatment effect (e.g. as defended in your Directed Acyclic Graph).
category_name	Character name of the variable indicating the categories over which the gap is defined. Must be the name of a column in data.
outcome_name	Character name of the outcome variable. Only required when there is no outcome_formula; otherwise extracted automatically. Must be a name of a column in data.
treatment_name	Character name of the treatment variable. Only required when there is no treatment_formula; otherwise extracted automatically. Must be a name of a column in data.
treatment_algorithm	Character name of the algorithm for the treatment model. One of "glm", "ridge", "gam", or "ranger". Defaults to "glm", which is a logit model. Option "ridge" is ridge regression. Option "gam" is a generalized additive model fit (see package mgcv). Option "ranger" is a random forest (see package ranger). If "ranger", this function avoids propensity scores equal to 0 or 1 by bottom- and top-coding predicted values at .001 and .999.
outcome_algorithm	Character name of the algorithm for the outcome model. One of "lm", "ridge", "gam", or "ranger". Defaults to "lm", which is an OLS model. Option "ridge" is ridge regression. Option "gam" is a generalized additive model fit (see package mgcv). Option "ranger" is a random forest (see package ranger).
weight_name	Character name of a sampling weight variable, if any, which captures the inverse probability of inclusion in the sample. The default assumes a simple random sample (all weights equal).

Value

@return A list with four elements.

counterfactual_means A tibble with a counterfactual mean estimate for each category counterfactual_means A tibble with a counterfactual disparity estimate for each pair of categories treatment_model Object containing the fitted treatment model outcome_model Object containing the fitted outcome model

References

Lundberg I (2021). "The gap-closing estimand: A causal approach to study interventions that close disparities across social categories." Sociological Methods and Research. Available at https://osf.io/gx4y3/.

gapclosing documentation built on Oct. 11, 2021, 9:07 a.m.