R/loop.R

In almost-matching-exactly/R-FLAME: Interpretable Matching for Causal Inference

run_AME <- function(data, active_cov_sets, processed_cov_sets,
                    early_stop_params, verbose, C, algo, weights, MGs, replace,
                    n_flame_iters, return_pe, return_bf, n_covs, holdout,
                    PE_method, user_PE_fit, user_PE_fit_params,
                    user_PE_predict, user_PE_predict_params, info) {
  iter <- 1

  if (is.null(weights)) {
    store_pe <- early_stop_params$baseline
  }
  else {
    store_pe <- 0
  }
  store_bf <- NULL

  # Covariate sets dropped
  all_cov_sets <- list(NULL)
  matches_out <- list(data = data, MGs = MGs, cov_sets = all_cov_sets)
  covs <- 1:n_covs

  while (!early_stop(iter, data, n_covs, active_cov_sets,
                     early_stop_params, verbose, algo)) {
    if (iter < n_flame_iters) {
      algo <- 'FLAME'
    }
    else {
      algo <- 'DAME'
    }
    iter <- iter + 1
    show_progress(verbose, iter, data, algo)

    # Find the best covariate set to drop
    cov_sets <-
      update_cov_sets(active_cov_sets, processed_cov_sets, covs, weights, C,
                      algo, data, holdout, PE_method, user_PE_fit,
                      user_PE_fit_params, user_PE_predict,
                      user_PE_predict_params, replace)

    curr_cov_set <- cov_sets$current
    active_cov_sets <- cov_sets$active
    processed_cov_sets <- cov_sets$processed

    PE <- cov_sets$PE
    BF <- cov_sets$BF

    ## Note for Vittorio: min(PE)?
    if (early_stop_PE(PE, early_stop_params, verbose, weights, algo)) {
      if (return_pe) {
        matches_out$PE <- store_pe
      }
      if (return_bf) {
        matches_out$BF <- store_bf
      }
      return(matches_out)
    }

    if (!is.null(BF)) {
      prop_unmatched <- BF[['prop_unmatched']]
    }

    if (early_stop_BF(BF$BF,
                      prop_unmatched[['control']], prop_unmatched[['treated']],
                      early_stop_params, verbose)) {

      if (return_pe) {
        matches_out$PE <- store_pe
      }
      if (return_bf) {
        matches_out$BF <- store_bf
      }
      return(matches_out)
    }

    if (is.null(weights)) {
      store_pe <- c(store_pe, PE)
    }
    else {
      store_pe <- c(store_pe, PE / sum(weights))
    }

    store_bf <- c(store_bf, BF$BF)

    # Make new matches having dropped a covariate
    ## Ideally should just return this from MQ so you don't have to redo it
    matches_out <- update_matches(data, replace, curr_cov_set,
                                  n_covs, MGs, all_cov_sets, info)
    data <- matches_out$data
    MGs <- matches_out$MGs
    all_cov_sets <- matches_out$cov_sets
  }
  if (return_pe) {
    matches_out$PE <- store_pe
  }
  if (return_bf) {
    matches_out$BF <- store_bf
  }
  return(matches_out)
}