diff --git a/docs/source/index.md b/docs/source/index.md
index 72025511..c9b8db5f 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -96,7 +96,7 @@ CausalPy has a broad range of quasi-experimental methods for causal inference:
 | Geographical lift | Measures the impact of an intervention in a specific geographic area by comparing it to similar areas without the intervention. Commonly used in marketing to assess regional campaigns. |
 | ANCOVA | Analysis of Covariance combines ANOVA and regression to control for the effects of one or more quantitative covariates. Used when comparing group means while controlling for other variables. |
 | Differences in Differences | Compares the changes in outcomes over time between a treatment group and a control group. Used in observational studies to estimate causal effects by accounting for time trends. |
-| Regression discontinuity | Identifies causal effects by exploiting a cutoff or threshold in an assignment variable. Used when treatment is assigned based on a threshold value of an observed variable, allowing comparison just above and below the cutoff. |
+| Sharp regression discontinuity | Identifies causal effects by exploiting a cutoff or threshold in an assignment variable. Used when treatment is assigned based on a threshold value of an observed variable, allowing comparison just above and below the cutoff. |
 | Regression kink designs | Focuses on changes in the slope (kinks) of the relationship between variables rather than jumps at cutoff points. Used to identify causal effects when treatment intensity changes at a threshold. |
 | Interrupted time series | Analyzes the effect of an intervention by comparing time series data before and after the intervention. Used when data is collected over time and an intervention occurs at a known point, allowing assessment of changes in level or trend. |
 | Instrumental variable regression | Addresses endogeneity by using an instrument variable that is correlated with the endogenous explanatory variable but uncorrelated with the error term. Used when explanatory variables are correlated with the error term, providing consistent estimates of causal effects. |
diff --git a/docs/source/notebooks/rd_pymc.ipynb b/docs/source/notebooks/rd_pymc.ipynb
index be23d3c4..a6fba749 100644
--- a/docs/source/notebooks/rd_pymc.ipynb
+++ b/docs/source/notebooks/rd_pymc.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Regression discontinuity with `pymc` models"
+    "# Sharp regression discontinuity with `pymc` models"
    ]
   },
   {
diff --git a/docs/source/notebooks/rd_pymc_drinking.ipynb b/docs/source/notebooks/rd_pymc_drinking.ipynb
index 4ffca0db..3fc80167 100644
--- a/docs/source/notebooks/rd_pymc_drinking.ipynb
+++ b/docs/source/notebooks/rd_pymc_drinking.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Drinking age - Bayesian analysis\n",
     "\n",
-    "This example uses the regression discontinuity design to make claims about the causal effects of the minimum legal drinking age (21 in the USA) upon all cause mortality rates. The dataset is from a study by {cite:t}`carpenter2009effect`."
+    "This example uses the Sharp regression discontinuity design to make claims about the causal effects of the minimum legal drinking age (21 in the USA) upon all cause mortality rates. The dataset is from a study by {cite:t}`carpenter2009effect`."
    ]
   },
   {
diff --git a/docs/source/notebooks/rd_skl.ipynb b/docs/source/notebooks/rd_skl.ipynb
index b790fad2..e38475b3 100644
--- a/docs/source/notebooks/rd_skl.ipynb
+++ b/docs/source/notebooks/rd_skl.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Regression discontinuity with sci-kit learn models"
+    "# Sharp regression discontinuity with sci-kit learn models"
    ]
   },
   {
diff --git a/docs/source/notebooks/rd_skl_drinking.ipynb b/docs/source/notebooks/rd_skl_drinking.ipynb
index 05adda46..75a89f05 100644
--- a/docs/source/notebooks/rd_skl_drinking.ipynb
+++ b/docs/source/notebooks/rd_skl_drinking.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Drinking age with a scikit-learn model\n",
     "\n",
-    "This example uses the regression discontinuity design to make claims about the causal effects of the minimum legal drinking age (21 in the USA) upon all cause mortality rates. The dataset is from a study by {cite:t}`carpenter2009effect`."
+    "This example uses the Sharp regression discontinuity design to make claims about the causal effects of the minimum legal drinking age (21 in the USA) upon all cause mortality rates. The dataset is from a study by {cite:t}`carpenter2009effect`."
    ]
   },
   {