diff --git a/DESCRIPTION b/DESCRIPTION
index bca85c1b9..32b84c61e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.1.0.9000
+Version: 1.1.0.9001
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
diff --git a/NAMESPACE b/NAMESPACE
index 63b4305b1..9eebfe33b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -145,6 +145,9 @@ S3method(update,svm_rbf)
 S3method(varying_args,model_spec)
 S3method(varying_args,recipe)
 S3method(varying_args,step)
+S3method(weight_propensity,default)
+S3method(weight_propensity,model_fit)
+S3method(weight_propensity,model_spec)
 export("%>%")
 export(.censoring_weights_graf)
 export(.check_glmnet_penalty_fit)
@@ -315,6 +318,7 @@ export(update_model_info_file)
 export(update_spec)
 export(varying)
 export(varying_args)
+export(weight_propensity)
 export(xgb_predict)
 export(xgb_train)
 importFrom(dplyr,arrange)
diff --git a/R/weight_propensity.R b/R/weight_propensity.R
new file mode 100644
index 000000000..616f5afee
--- /dev/null
+++ b/R/weight_propensity.R
@@ -0,0 +1,90 @@
+#' Helper for bridging two-stage causal fits
+#'
+#' @description
+#' `weight_propensity()` is a helper function to more easily link the
+#' propensity and outcome models in causal workflows. **The main documentation
+#' for this function lives in the tune package at** `?tune::weight_propensity`.
+#'
+#' @param object The object containing the model fit(s) that will generate
+#' predictions used to calculate propensity weights. Currently, either a
+#' [parsnip model fit][parsnip::fit.model_spec()], fitted
+#' [workflow][workflows::workflow()], or
+#' tuning results (`?tune::fit_resamples`) object. If a tuning result, the
+#' object must have been generated with the control argument
+#' (`?tune::control_resamples`) `extract = identity`.
+#' @param wt_fn A function used to calculate the propensity weights. The first
+#' argument gives the predicted probability of exposure, the true value for
+#' which is provided in the second argument. See `?propensity::wt_ate()` for
+#' an example.
+#' @param .treated The level of the exposure corresponding to the treatment, as
+#' a string. Additionally passed as `.treated` to `wt_fn`.
+#' @param ... Additional arguments passed to `wt_fn`.
+#' @param data The data supplied as the `data` argument to `fit()` the `object`.
+#' This argument is only required for the `model_fit` and `workflow` methods---the
+#' needed data for the `tune_results` method lives inside of `object`.
+#'
+#' @return
+#' For `model_fit` and fitted `workflow` input, a modified version of the data
+#' set supplied in `data` that contains a `.wts` column with class
+#' `importance_weights`. For `tune_results` input, a modified version of the
+#' resampling object underlying the tuning results containing a new `.wts` column
+#' with propensity values corresponding to each element of the analysis set.
+#'
+#' @references Barrett M & D'Agostino McGowan L (forthcoming).
+#' _Causal Inference in R_. \url{https://www.r-causal.org/}
+#' @name weight_propensity.model_fit
+NULL
+
+#' @rdname weight_propensity.model_fit
+#' @export
+weight_propensity <- function(object, wt_fn, ...) {
+  UseMethod("weight_propensity")
+}
+
+#' @rdname weight_propensity.model_fit
+#' @method weight_propensity default
+#' @export
+weight_propensity.default <- function(object, wt_fn, ...) {
+  abort("No known `weight_propensity()` method for this type of object.")
+}
+
+#' @noRd
+#' @method weight_propensity model_spec
+#' @export
+weight_propensity.model_spec <- function(object, wt_fn, ...) {
+  abort(c(
+    "`weight_propensity()` is not well-defined for a model specification.",
+    "i" = "Supply `object` to `fit()` before generating propensity weights."
+  ))
+}
+
+#' @rdname weight_propensity.model_fit
+#' @method weight_propensity model_fit
+#' @export
+weight_propensity.model_fit <- function(object,
+                                        wt_fn,
+                                        .treated = object$lvl[2],
+                                        ...,
+                                        data) {
+  if (rlang::is_missing(wt_fn) || !is.function(wt_fn)) {
+    abort("`wt_fn` must be a function.")
+  }
+
+  if (rlang::is_missing(data) || !is.data.frame(data)) {
+    abort("`data` must be the data supplied as the data argument to `fit()`.")
+  }
+
+  # TODO: I'm not sure we have a way to identify `y` via a model
+  #       spec fitted with `fit_xy()`---this will error in that case.
+  outcome_name <- object$preproc$y_var
+
+  preds <- predict(object, data, type = "prob")
+  preds <- preds[[paste0(".pred_", .treated)]]
+
+  data$.wts <-
+    hardhat::importance_weights(
+      wt_fn(preds, data[[outcome_name]], .treated = .treated, ...)
+    )
+
+  data
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 12fe60196..b2f0b4358 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -92,6 +92,7 @@ reference:
       - translate
       - starts_with("update")
       - matches("_train")
+      - starts_with("weight_propensity")
 
   - title: Developer tools
     contents:
diff --git a/man/weight_propensity.model_fit.Rd b/man/weight_propensity.model_fit.Rd
new file mode 100644
index 000000000..4154bb217
--- /dev/null
+++ b/man/weight_propensity.model_fit.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/weight_propensity.R
+\name{weight_propensity.model_fit}
+\alias{weight_propensity.model_fit}
+\alias{weight_propensity}
+\alias{weight_propensity.default}
+\title{Helper for bridging two-stage causal fits}
+\usage{
+weight_propensity(object, wt_fn, ...)
+
+\method{weight_propensity}{default}(object, wt_fn, ...)
+
+\method{weight_propensity}{model_fit}(object, wt_fn, .treated = object$lvl[2], ..., data)
+}
+\arguments{
+\item{object}{The object containing the model fit(s) that will generate
+predictions used to calculate propensity weights. Currently, either a
+\link[=fit.model_spec]{parsnip model fit}, fitted
+\link[workflows:workflow]{workflow}, or
+tuning results (\code{?tune::fit_resamples}) object. If a tuning result, the
+object must have been generated with the control argument
+(\code{?tune::control_resamples}) \code{extract = identity}.}
+
+\item{wt_fn}{A function used to calculate the propensity weights. The first
+argument gives the predicted probability of exposure, the true value for
+which is provided in the second argument. See \code{?propensity::wt_ate()} for
+an example.}
+
+\item{...}{Additional arguments passed to \code{wt_fn}.}
+
+\item{.treated}{The level of the exposure corresponding to the treatment, as
+a string. Additionally passed as \code{.treated} to \code{wt_fn}.}
+
+\item{data}{The data supplied as the \code{data} argument to \code{fit()} the \code{object}.
+This argument is only required for the \code{model_fit} and \code{workflow} methods---the
+needed data for the \code{tune_results} method lives inside of \code{object}.}
+}
+\value{
+For \code{model_fit} and fitted \code{workflow} input, a modified version of the data
+set supplied in \code{data} that contains a \code{.wts} column with class
+\code{importance_weights}. For \code{tune_results} input, a modified version of the
+resampling object underlying the tuning results containing a new \code{.wts} column
+with propensity values corresponding to each element of the analysis set.
+}
+\description{
+\code{weight_propensity()} is a helper function to more easily link the
+propensity and outcome models in causal workflows. \strong{The main documentation
+for this function lives in the tune package at} \code{?tune::weight_propensity}.
+}
+\references{
+Barrett M & D'Agostino McGowan L (forthcoming).
+\emph{Causal Inference in R}. \url{https://www.r-causal.org/}
+}
diff --git a/tests/testthat/_snaps/weight_propensity.md b/tests/testthat/_snaps/weight_propensity.md
new file mode 100644
index 000000000..d6631bd11
--- /dev/null
+++ b/tests/testthat/_snaps/weight_propensity.md
@@ -0,0 +1,59 @@
+# errors informatively with bad input
+
+    Code
+      weight_propensity(spec, silly_wt_fn, data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `weight_propensity()` is not well-defined for a model specification.
+      i Supply `object` to `fit()` before generating propensity weights.
+
+---
+
+    Code
+      weight_propensity("boop", silly_wt_fn, data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! No known `weight_propensity()` method for this type of object.
+
+---
+
+    Code
+      weight_propensity(spec_fit, two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(spec_fit, "boop", data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(spec_fit, function(...) {
+        -1L
+      }, data = two_class_dat)
+    Condition
+      Error in `hardhat::importance_weights()`:
+      ! `x` can't contain negative weights.
+
+---
+
+    Code
+      weight_propensity(spec_fit, silly_wt_fn)
+    Condition
+      Error in `weight_propensity()`:
+      ! `data` must be the data supplied as the data argument to `fit()`.
+
+---
+
+    Code
+      weight_propensity(spec_fit, silly_wt_fn, data = "boop")
+    Condition
+      Error in `weight_propensity()`:
+      ! `data` must be the data supplied as the data argument to `fit()`.
+
diff --git a/tests/testthat/test-weight_propensity.R b/tests/testthat/test-weight_propensity.R
new file mode 100644
index 000000000..9df49527c
--- /dev/null
+++ b/tests/testthat/test-weight_propensity.R
@@ -0,0 +1,68 @@
+test_that("basic functionality", {
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    seq(1, 2, length.out = length(.propensity))
+  }
+
+  lr_fit <- fit(logistic_reg(), Class ~ A + B, two_class_dat)
+
+  lr_res1 <- weight_propensity(lr_fit, silly_wt_fn, data = two_class_dat)
+  expect_s3_class(lr_res1, "tbl_df")
+  expect_true(all(names(lr_res1) %in% c(names(two_class_dat), ".wts")))
+  expect_equal(lr_res1$.wts, importance_weights(seq(1, 2, length.out = nrow(two_class_dat))))
+})
+
+test_that("errors informatively with bad input", {
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    seq(1, 2, length.out = length(.propensity))
+  }
+
+  # bad `object`
+  spec <- logistic_reg()
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec, silly_wt_fn, data = two_class_dat)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity("boop", silly_wt_fn, data = two_class_dat)
+  )
+
+  # bad `wt_fn`
+  spec_fit <- fit(spec, Class ~ A + B, data = two_class_dat)
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec_fit, two_class_dat)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec_fit, "boop", data = two_class_dat)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec_fit, function(...) {-1L}, data = two_class_dat)
+  )
+
+  # bad `data`
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec_fit, silly_wt_fn)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(spec_fit, silly_wt_fn, data = "boop")
+  )
+})