diff --git a/README.Rmd b/README.Rmd
index 456545a..b676586 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -34,27 +34,42 @@ devtools::install_github("r-causal/propensity")
 
 ## Example
 
-propensity is under very early development. Currently, it supports calculating propensity score weights for binary exposures:
+Currently, propensity supports calculating propensity score weights for binary exposures.
+
+There are [5 common types of weights](https://www.r-causal.org/chapters/chapter-10.html) that you might wish to use in a propensity score setting: 
+
+1. **Average treatment effect (ATE):** used when the target population is the entire population of interest. An example question this answers is "Should a marketing campaign be rolled out to all eligible people?"
+
+2. **Average treatment effect among the treated (ATT):** used when the target population is the exposed/treated population. An example question this answers is "Should we stop our marketing campaign to those currently receiving it?"
+
+3. **Average treatment effect among the unexposed (ATU):** used when the target population is the unexposed/untreated/control population. An example question this answers is "Should we send our marketing campaign to those not currently receiving it?"
+
+4. **Average treatment effect among the evenly matchable (ATM):** used when the target population is those deemed “evenly matchable” by some distance metric. An example question this answers is "Should we send our marketing campaign to those of with similar demographic characteristics?"
+
+5. **Average treatment effect among the overlap (ATO):** used when the target population is the same as the ATM setting, however the ATO weights are slightly attenuated with improved variance properties.
+
+Each of these weights can optionally be "stabilized" to prevent extreme weights that lead to wide confidence intervals. 
+
+The below example shows how we would generate ATE and ATO weights for 4 participants (1 exposed and 3 unexposed) with known probabilities of being exposed (propensity scores). 
 
 ```{r}
 library(propensity)
+
 propensity_scores <- c(.1, .3, .4, .3)
 x <- c(0, 0, 1, 0)
 
-# ATE weights
+# Average treatment effect (ATE) weights
 wt_ate(propensity_scores, .exposure = x)
 
 # Stabilized ATE weights
 wt_ate(propensity_scores, .exposure = x, stabilize = TRUE)
 
-# ATO weights
+# Average treatment effect in the overlap (ATO) weights
 wt_ato(propensity_scores, .exposure = x)
 ```
 
 
 ```{r example, include = FALSE, eval = FALSE}
-library(propensity)
-
 ps <- propensity(
   model, 
   exposure, 
diff --git a/README.md b/README.md
index 8f5e4f9..063cfe6 100644
--- a/README.md
+++ b/README.md
@@ -25,15 +25,52 @@ devtools::install_github("r-causal/propensity")
 
 ## Example
 
-propensity is under very early development. Currently, it supports
-calculating propensity score weights for binary exposures:
+Currently, propensity supports calculating propensity score weights for
+binary exposures.
+
+There are [5 common types of
+weights](https://www.r-causal.org/chapters/chapter-10.html) that you
+might wish to use in a propensity score setting:
+
+1.  **Average treatment effect (ATE):** used when the target population
+    is the entire population of interest. An example question this
+    answers is “Should a marketing campaign be rolled out to all
+    eligible people?”
+
+2.  **Average treatment effect among the treated (ATT):** used when the
+    target population is the exposed/treated population. An example
+    question this answers is “Should we stop our marketing campaign to
+    those currently receiving it?”
+
+3.  **Average treatment effect among the unexposed (ATU):** used when
+    the target population is the unexposed/untreated/control population.
+    An example question this answers is “Should we send our marketing
+    campaign to those not currently receiving it?”
+
+4.  **Average treatment effect among the evenly matchable (ATM):** used
+    when the target population is those deemed “evenly matchable” by
+    some distance metric. An example question this answers is “Should we
+    send our marketing campaign to those of with similar demographic
+    characteristics?”
+
+5.  **Average treatment effect among the overlap (ATO):** used when the
+    target population is the same as the ATM setting, however the ATO
+    weights are slightly attenuated with improved variance properties.
+
+Each of these weights can optionally be “stabilized” to prevent extreme
+weights that lead to wide confidence intervals.
+
+The below example shows how we would generate ATE and ATO weights for 4
+participants (1 exposed and 3 unexposed) with known probabilities of
+being exposed (propensity scores).
 
 ``` r
 library(propensity)
+
 propensity_scores <- c(.1, .3, .4, .3)
 x <- c(0, 0, 1, 0)
 
-# ATE weights
+# Average treatment effect (ATE) weights
 wt_ate(propensity_scores, .exposure = x)
 #> [1] 1.111111 1.428571 2.500000 1.428571
 
@@ -41,7 +78,7 @@ wt_ate(propensity_scores, .exposure = x)
 wt_ate(propensity_scores, .exposure = x, stabilize = TRUE)
 #> [1] 0.2777778 0.3571429 0.6250000 0.3571429
 
-# ATO weights
+# Average treatment effect in the overlap (ATO) weights
 wt_ato(propensity_scores, .exposure = x)
 #> [1] 0.1 0.3 0.6 0.3
 ```