diff --git a/docs/source/independence_tests_index/kci.rst b/docs/source/independence_tests_index/kci.rst
index f3f7351..1123050 100644
--- a/docs/source/independence_tests_index/kci.rst
+++ b/docs/source/independence_tests_index/kci.rst
@@ -52,7 +52,7 @@ and n_features is the number of features.
 
 + Either for specifying parameters of KCI, including:
 
-  **KernelX/Y/Z (condition_set)**: ['GaussianKernel', 'LinearKernel', 'PolynomialKernel']. (For 'PolynomialKernel', the default degree is 2. Currently, users can change it by setting the 'degree' of 'class PolynomialKernel()'.
+  **KernelX/Y/Z (condition_set)**: ['Gaussian', 'Linear', 'Polynomial']. (For 'Polynomial', the default degree is 2. Currently, users can change it by setting the 'degree' of 'class PolynomialKernel()'.
 
   **est_width**: set kernel width for Gaussian kernels.
    - 'empirical': set kernel width using empirical rules (default).
@@ -72,4 +72,4 @@ Returns
 **p**: the p value.
 
 
-.. [1] Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011, July). Kernel-based Conditional Independence Test and Application in Causal Discovery. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 804-813). AUAI Press.
\ No newline at end of file
+.. [1] Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011, July). Kernel-based Conditional Independence Test and Application in Causal Discovery. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 804-813). AUAI Press.