diff --git a/DESCRIPTION b/DESCRIPTION
index 822d05d6..984f3fb7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.2
+Version: 0.1.3
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NAMESPACE b/NAMESPACE
index 62e00b07..0f5b385b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -252,6 +252,7 @@ importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,group_by_at)
+importFrom(dplyr,inner_join)
 importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
@@ -283,6 +284,7 @@ importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
+importFrom(magrittr,extract2)
 importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
diff --git a/R/autoplot.R b/R/autoplot.R
index 4f422297..870dcb8d 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -127,11 +127,10 @@ autoplot.epi_workflow <- function(
   if (!is.null(shift)) {
     edf <- mutate(edf, time_value = time_value + shift)
   }
-  extra_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
-  if (length(extra_keys) == 0L) extra_keys <- NULL
+  other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
   edf <- as_epi_df(edf,
     as_of = object$fit$meta$as_of,
-    other_keys = extra_keys %||% character()
+    other_keys = other_keys
   )
   if (is.null(predictions)) {
     return(autoplot(
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index 3dee263e..b4b9973b 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -7,7 +7,9 @@
 #' @importFrom cli cli_abort cli_warn
 #' @importFrom dplyr arrange across all_of any_of bind_cols bind_rows group_by
 #' @importFrom dplyr full_join relocate summarise everything
+#' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
+#' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
 #' @importFrom stats poly predict lm residuals quantile
diff --git a/R/key_colnames.R b/R/key_colnames.R
index b9ebde5d..b8d07ce8 100644
--- a/R/key_colnames.R
+++ b/R/key_colnames.R
@@ -1,20 +1,22 @@
 #' @export
-key_colnames.recipe <- function(x, ...) {
+key_colnames.recipe <- function(x, ..., exclude = character()) {
   geo_key <- x$var_info$variable[x$var_info$role %in% "geo_value"]
   time_key <- x$var_info$variable[x$var_info$role %in% "time_value"]
   keys <- x$var_info$variable[x$var_info$role %in% "key"]
-  c(geo_key, keys, time_key) %||% character(0L)
+  full_key <- c(geo_key, keys, time_key) %||% character(0L)
+  full_key[!full_key %in% exclude]
 }
 
 #' @export
-key_colnames.epi_workflow <- function(x, ...) {
+key_colnames.epi_workflow <- function(x, ..., exclude = character()) {
   # safer to look at the mold than the preprocessor
   mold <- hardhat::extract_mold(x)
   molded_names <- names(mold$extras$roles)
   geo_key <- names(mold$extras$roles[molded_names %in% "geo_value"]$geo_value)
   time_key <- names(mold$extras$roles[molded_names %in% "time_value"]$time_value)
   keys <- names(mold$extras$roles[molded_names %in% "key"]$key)
-  c(geo_key, keys, time_key) %||% character(0L)
+  full_key <- c(geo_key, keys, time_key) %||% character(0L)
+  full_key[!full_key %in% exclude]
 }
 
 kill_time_value <- function(v) {
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 4755083c..652abfd0 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -135,6 +135,25 @@ slather.layer_population_scaling <-
     )
     rlang::check_dots_empty()
 
+    if (is.null(object$by)) {
+      # Assume `layer_predict` has calculated the prediction keys and other
+      # layers don't change the prediction key colnames:
+      prediction_key_colnames <- names(components$keys)
+      lhs_potential_keys <- prediction_key_colnames
+      rhs_potential_keys <- colnames(select(object$df, !object$df_pop_col))
+      object$by <- intersect(lhs_potential_keys, rhs_potential_keys)
+      suggested_min_keys <- kill_time_value(lhs_potential_keys)
+      if (!all(suggested_min_keys %in% object$by)) {
+        cli_warn(c(
+          "Couldn't find {setdiff(suggested_min_keys, object$by)} in population `df`",
+          "i" = "Defaulting to join by {object$by}",
+          ">" = "Double-check whether column names on the population `df` match those expected in your predictions",
+          ">" = "Consider using population data with breakdowns by {suggested_min_keys}",
+          ">" = "Manually specify `by =` to silence"
+        ), class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys")
+      }
+    }
+
     object$by <- object$by %||% intersect(
       epi_keys_only(components$predictions),
       colnames(select(object$df, !object$df_pop_col))
@@ -152,10 +171,12 @@ slather.layer_population_scaling <-
     suffix <- ifelse(object$create_new, object$suffix, "")
     col_to_remove <- setdiff(colnames(object$df), colnames(components$predictions))
 
-    components$predictions <- left_join(
+    components$predictions <- inner_join(
       components$predictions,
       object$df,
       by = object$by,
+      relationship = "many-to-one",
+      unmatched = c("error", "drop"),
       suffix = c("", ".df")
     ) %>%
       mutate(across(
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 9e653184..1388dd85 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -112,7 +112,7 @@ make_quantile_reg <- function() {
 
     # can't make a method because object is second
     out <- switch(type,
-      rq = dist_quantiles(unname(as.list(x)), object$quantile_levels), # one quantile
+      rq = dist_quantiles(unname(as.list(x)), object$tau), # one quantile
       rqs = {
         x <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
         dist_quantiles(x, list(object$tau))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 3d3e6529..a3f7bf50 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -89,13 +89,25 @@ step_population_scaling <-
            suffix = "_scaled",
            skip = FALSE,
            id = rand_id("population_scaling")) {
-    arg_is_scalar(role, df_pop_col, rate_rescaling, create_new, suffix, id)
-    arg_is_lgl(create_new, skip)
-    arg_is_chr(df_pop_col, suffix, id)
+    if (rlang::dots_n(...) == 0L) {
+      cli_abort(c(
+        "`...` must not be empty.",
+        ">" = "Please provide one or more tidyselect expressions in `...`
+               specifying the columns to which scaling should be applied.",
+        ">" = "If you really want to list `step_population_scaling` in your
+               recipe but not have it do anything, you can use a tidyselection
+               that selects zero variables, such as `c()`."
+      ))
+    }
+    arg_is_scalar(role, df_pop_col, rate_rescaling, create_new, suffix, skip, id)
+    arg_is_chr(role, df_pop_col, suffix, id)
+    hardhat::validate_column_names(df, df_pop_col)
     arg_is_chr(by, allow_null = TRUE)
+    arg_is_numeric(rate_rescaling)
     if (rate_rescaling <= 0) {
       cli_abort("`rate_rescaling` must be a positive number.")
     }
+    arg_is_lgl(create_new, skip)
 
     recipes::add_step(
       recipe,
@@ -138,6 +150,41 @@ step_population_scaling_new <-
 
 #' @export
 prep.step_population_scaling <- function(x, training, info = NULL, ...) {
+  if (is.null(x$by)) {
+    rhs_potential_keys <- setdiff(colnames(x$df), x$df_pop_col)
+    lhs_potential_keys <- info %>%
+      filter(role %in% c("geo_value", "key", "time_value")) %>%
+      extract2("variable") %>%
+      unique() # in case of weird var with multiple of above roles
+    if (length(lhs_potential_keys) == 0L) {
+      # We're working with a recipe and tibble, and *_role hasn't set up any of
+      # the above roles. Let's say any column could actually act as a key, and
+      # lean on `intersect` below to make this something reasonable.
+      lhs_potential_keys <- names(training)
+    }
+    suggested_min_keys <- info %>%
+      filter(role %in% c("geo_value", "key")) %>%
+      extract2("variable") %>%
+      unique()
+    # (0 suggested keys if we weren't given any epikeytime var info.)
+    x$by <- intersect(lhs_potential_keys, rhs_potential_keys)
+    if (length(x$by) == 0L) {
+      cli_stop(c(
+        "Couldn't guess a default for `by`",
+        ">" = "Please rename columns in your population data to match those in your training data,
+               or manually specify `by =` in `step_population_scaling()`."
+      ), class = "epipredict__step_population_scaling__default_by_no_intersection")
+    }
+    if (!all(suggested_min_keys %in% x$by)) {
+      cli_warn(c(
+        "Couldn't find {setdiff(suggested_min_keys, x$by)} in population `df`.",
+        "i" = "Defaulting to join by {x$by}.",
+        ">" = "Double-check whether column names on the population `df` match those for your time series.",
+        ">" = "Consider using population data with breakdowns by {suggested_min_keys}.",
+        ">" = "Manually specify `by =` to silence."
+      ), class = "epipredict__step_population_scaling__default_by_missing_suggested_keys")
+    }
+  }
   step_population_scaling_new(
     terms = x$terms,
     role = x$role,
@@ -156,10 +203,6 @@ prep.step_population_scaling <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_population_scaling <- function(object, new_data, ...) {
-  object$by <- object$by %||% intersect(
-    epi_keys_only(new_data),
-    colnames(select(object$df, !object$df_pop_col))
-  )
   joinby <- list(x = names(object$by) %||% object$by, y = object$by)
   hardhat::validate_column_names(new_data, joinby$x)
   hardhat::validate_column_names(object$df, joinby$y)
@@ -177,7 +220,9 @@ bake.step_population_scaling <- function(object, new_data, ...) {
   suffix <- ifelse(object$create_new, object$suffix, "")
   col_to_remove <- setdiff(colnames(object$df), colnames(new_data))
 
-  left_join(new_data, object$df, by = object$by, suffix = c("", ".df")) %>%
+  inner_join(new_data, object$df,
+             by = object$by, relationship = "many-to-one", unmatched = c("error", "drop"),
+             suffix = c("", ".df")) %>%
     mutate(
       across(
         all_of(object$columns),
diff --git a/R/utils-misc.R b/R/utils-misc.R
index a1e0f025..7f1eaf84 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -40,7 +40,7 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   # 2. these are the keys in the training data
   old_keys <- key_colnames(workflow)
   # 3. these are the keys in the test data as input
-  new_df_keys <- key_colnames(new_data, extra_keys = setdiff(new_keys, c("geo_value", "time_value")))
+  new_df_keys <- key_colnames(new_data, other_keys = setdiff(new_keys, c("geo_value", "time_value")))
   if (!(setequal(old_keys, new_df_keys) && setequal(new_keys, new_df_keys))) {
     cli_warn(paste(
       "Not all epi keys that were present in the training data are available",
@@ -49,10 +49,10 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   }
   if (is_epi_df(new_data)) {
     meta <- attr(new_data, "metadata")
-    extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys %||% character())
+    extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys)
   } else if (all(c("geo_value", "time_value") %in% new_keys)) {
-    if (length(new_keys) > 2) other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
-    extras <- as_epi_df(extras, other_keys = other_keys %||% character())
+    other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
+    extras <- as_epi_df(extras, other_keys = other_keys)
   }
   extras
 }
diff --git a/tests/testthat/test-key_colnames.R b/tests/testthat/test-key_colnames.R
index d94daaec..021bbb50 100644
--- a/tests/testthat/test-key_colnames.R
+++ b/tests/testthat/test-key_colnames.R
@@ -17,6 +17,9 @@ test_that("key_colnames extracts time_value and geo_value, but not raw", {
     fit(data = covid_case_death_rates)
 
   expect_identical(key_colnames(my_workflow), c("geo_value", "time_value"))
+
+  # `exclude =` works:
+  expect_identical(key_colnames(my_workflow, exclude = "geo_value"), c("time_value"))
 })
 
 test_that("key_colnames extracts additional keys when they are present", {
@@ -49,4 +52,7 @@ test_that("key_colnames extracts additional keys when they are present", {
 
   # order of the additional keys may be different
   expect_equal(key_colnames(my_workflow), c("geo_value", "state", "pol", "time_value"))
+
+  # `exclude =` works:
+  expect_equal(key_colnames(my_workflow, exclude = c("time_value", "pol")), c("geo_value", "state"))
 })
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 966d703d..88bbd4ed 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -304,6 +304,238 @@ test_that("test joining by default columns", {
 })
 
 
+test_that("test joining by default columns with less common keys/classes", {
+  # Make a model spec that expects no predictor columns and outputs a fixed
+  # (rate) prediction. Based on combining two linear inequalities.
+  fixed_rate_prediction <- 2e-6
+  model_spec <- quantile_reg(quantile_levels = 0.5, method = "fnc") %>%
+    set_engine(
+      "rq",
+      R = matrix(c(1, -1), 2, 1), r = c(1, -1) * fixed_rate_prediction,
+      eps = fixed_rate_prediction * 1e-6 # prevent early stop
+    )
+
+  # Here's the typical setup
+  dat1 <- tibble::tibble(geo_value = 1:2, time_value = 1, y = c(3 * 5, 7 * 11)) %>%
+    as_epi_df()
+  pop1 <- tibble::tibble(geo_value = 1:2, population = c(5e6, 11e6))
+  ewf1 <- epi_workflow(
+    epi_recipe(dat1) %>%
+      step_population_scaling(y, df = pop1, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf1, estimated = FALSE) %>%
+      prep(dat1) %>%
+      bake(new_data = NULL),
+    dat1 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf1, dat1)) %>%
+      pivot_quantiles_wider(.pred),
+    dat1 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      mutate(`0.5` = c(2 * 5, 2 * 11))
+  )
+
+  # With geo x age in time series but only geo in population data:
+  dat1b <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1) %>%
+    as_epi_df(other_keys = "age_group")
+  pop1b <- pop1
+  ewf1b <- epi_workflow(
+    epi_recipe(dat1b) %>%
+      step_population_scaling(y, df = pop1b, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1b, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_warning(
+    expect_equal(
+      extract_recipe(ewf1b, estimated = FALSE) %>%
+        prep(dat1b) %>%
+        bake(new_data = NULL),
+      dat1b %>%
+        # geo 1 scaling used for both:
+        mutate(y_scaled = c(3e-6, 7 * 11 / 5e6), ahead_0_y_scaled = y_scaled)
+    ),
+    class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+  )
+  expect_warning(
+    expect_warning(
+      expect_equal(
+        forecast(fit(ewf1b, dat1b)) %>%
+          pivot_quantiles_wider(.pred),
+        dat1b %>%
+          select(!"y") %>%
+          as_tibble() %>%
+          # geo 1 scaling used for both:
+          mutate(`0.5` = c(2 * 5, 2 * 5))
+      ),
+      class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+    ),
+    class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
+  )
+
+  # Same thing but with time series in tibble:
+  dat1bb <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1)
+  pop1bb <- pop1b
+  ewf1bb <- epi_workflow(
+    # Can't use epi_recipe or step_epi_ahead; adjust.
+    recipe(dat1bb) %>%
+    update_role("geo_value", new_role = "geo_value") %>%
+    update_role("age_group", new_role = "key") %>%
+    update_role("time_value", new_role = "time_value") %>%
+    step_population_scaling(y, df = pop1bb, df_pop_col = "population", role = "outcome") %>%
+      # XXX key_colnames inference differs at fit vs. predict time, so we also
+      # need to manually provide some key role settings to not have trouble at
+      # predict time.
+   {.},
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1bb, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf1bb, estimated = FALSE) %>%
+      prep(dat1bb) %>%
+      bake(new_data = NULL),
+    dat1bb %>%
+      # geo 1 scaling used for both:
+      mutate(y_scaled = c(3e-6, 7 * 11 / 5e6))
+  )
+  expect_equal(
+    predict(fit(ewf1bb, dat1bb), dat1bb) %>%
+      pivot_quantiles_wider(.pred),
+    dat1bb %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      # geo 1 scaling used for both:
+      mutate(`0.5` = c(2 * 5, 2 * 5))
+  )
+
+  # With geo x age_group breakdown on both:
+  dat2 <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1) %>%
+    as_epi_df(other_keys = "age_group")
+  pop2 <- pop1 %>%
+    mutate(age_group = geo_value, geo_value = 1)
+  ewf2 <- epi_workflow(
+    epi_recipe(dat2) %>%
+      step_population_scaling(y, df = pop2, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop2, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf2, estimated = FALSE) %>%
+      prep(dat2) %>%
+      bake(new_data = NULL),
+    dat2 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf2, dat2)) %>%
+      pivot_quantiles_wider(.pred),
+    dat2 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      mutate(`0.5` = c(2 * 5, 2 * 11))
+  )
+
+  # With only an age column in population data:
+  dat2b <- dat2
+  pop2b <- pop1 %>%
+    mutate(age_group = geo_value, geo_value = NULL)
+  ewf2b <- epi_workflow(
+    epi_recipe(dat2b) %>%
+      step_population_scaling(y, df = pop2b, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop2b, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_warning(
+    expect_equal(
+      extract_recipe(ewf2b, estimated = FALSE) %>%
+        prep(dat2b) %>%
+        bake(new_data = NULL),
+      dat2b %>%
+        mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+    ),
+    class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+  )
+  expect_warning(
+    expect_warning(
+      expect_equal(
+        forecast(fit(ewf2b, dat2b)) %>%
+          pivot_quantiles_wider(.pred),
+        dat2b %>%
+          select(!"y") %>%
+          as_tibble() %>%
+          mutate(`0.5` = c(2 * 5, 2 * 11))
+      ),
+      class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+    ),
+    class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
+  )
+
+  # with geo x time_value breakdown instead:
+  dat3 <- dat1 %>%
+    as_tibble() %>%
+    mutate(time_value = geo_value, geo_value = 1) %>%
+    as_epi_df()
+  pop3 <- pop1 %>%
+    mutate(time_value = geo_value, geo_value = 1)
+  ewf3 <- epi_workflow(
+    epi_recipe(dat3) %>%
+      step_population_scaling(y, df = pop3, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop3, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf3, estimated = FALSE) %>%
+      prep(dat3) %>%
+      bake(new_data = NULL),
+    dat3 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf3, dat3)) %>%
+      pivot_quantiles_wider(.pred),
+    # slightly edited copy-pasta due to test time selection:
+    dat3 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      slice_max(by = geo_value, time_value) %>%
+      mutate(`0.5` = 2 * 11)
+  )
+
+  # TODO non-`epi_df` scaling?
+
+  # TODO multikey scaling?
+
+})
+
+
 test_that("expect error if `by` selector does not match", {
   jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%