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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,72 @@ | ||
| #' Calculates propensity score weights. | ||
| #' | ||
| #' @param treatment a logical vector for treatment status. | ||
| #' @param ps numeric vector of propensity scores | ||
| #' @param estimand character string indicating which estimand to be used. Possible | ||
| #' values are | ||
| #' ATE (average treatment effect), | ||
| #' ATT (average treatment effect for the treated), | ||
| #' ATC (average treatement effect for the controls), | ||
| #' ATM (Average Treatment Effect Among the Evenly Matchable), | ||
| #' ATO (Average Treatment Effect Among the Overlap Populatio) | ||
| calculate_ps_weights <- function(treatment, ps, estimand = 'ATE') { | ||
| # TODO: this is a copy of a function from the psa package. Use that once it is on CRAN | ||
| weights <- NA | ||
| if(estimand == 'ATE') { | ||
| weights <- (treatment / ps) + ((1 - treatment) / (1 - ps)) | ||
| } else if(estimand == 'ATT') { | ||
| weights <- ((ps * treatment) / ps) + ((ps * (1 - treatment)) / (1 - ps)) | ||
| } else if(estimand == 'ATC') { | ||
| weights <- (((1 - ps) * treatment) / ps) + (((1 - ps) * (1 - treatment)) / (1 - ps)) | ||
| } else if(estimand == 'ATM') { | ||
| weights <- pmin(ps, 1 - ps) / (treatment * ps + (1 - treatment) * (1 - ps)) | ||
| } else if(estimand == 'ATO') { | ||
| weights <- (1 - ps) * treatment + ps * (1 - treatment) | ||
| } else { | ||
| stop(paste0('Invalid estimand specified: ', estimand)) | ||
| } | ||
| return(weights) | ||
| } | ||
|
|
||
| #' Propensity score weighting implementation for bootstrapping. | ||
| #' | ||
| #' @inherit boot.strata return params | ||
| #' @param estimand which treatment effect to estimate. Values can be ATE, ATT, | ||
| #' ATC, or ATM. | ||
| #' @importFrom stats quasibinomial binomial lm | ||
| #' @export | ||
| boot.weighting <- function(Tr, Y, X, X.trans, formu, estimand = 'ATE', ...) { | ||
| formu.treat <- update.formula(formu, 'treat ~ .') | ||
| df <- cbind(treat = Tr, X) | ||
| ps <- fitted(glm(formu.treat, | ||
| data = df, | ||
| family = binomial(link = 'logit'))) | ||
|
|
||
| weights <- calculate_ps_weights(treatment = Tr, | ||
| ps = ps, | ||
| estimand = estimand) | ||
|
|
||
| X.trans.scaled <- lapply(X.trans, scale) |> as.data.frame() | ||
| X.trans.scaled$treat <- Tr | ||
| lm_balance <- glm(treat ~ ., | ||
| data = X.trans.scaled, | ||
| family = quasibinomial(link = 'logit'), | ||
| weights = weights) |> summary() | ||
|
|
||
| te_lm <- lm(formula = Y ~ treat, | ||
| data = data.frame(Y = Y, treat = Tr), | ||
| weights = weights) |> summary() | ||
|
|
||
| se <- te_lm$coefficients[2,]['Std. Error'] |> unname() | ||
| te <- te_lm$coefficients[2,]['Estimate'] |> unname() | ||
|
|
||
| return(list( | ||
| summary = c(estimate = te, | ||
| ci.min = te - 1.96 * se, | ||
| ci.max = te + 1.96 * se, | ||
| se.wtd = se, | ||
| approx.t = unname(te_lm$coefficients[2,]['t value'])), | ||
| details = te_lm, | ||
| balance = lm_balance$coefficients[,'Estimate'][-1] | ||
| )) | ||
| } |
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