update demo

README.md

@@ -39,11 +39,11 @@ must be adjusted for as part of any causal inference analysis. That is where
 in non-randomized populations. In `pybalance`, we start with measures of "balance"
 (how similar two populations are) and directly optimize this metric. This approach is
 different, and we think almost always better, from the well-known propensity score
-approach (see our [demo](https://bayer-group.github.io/pybalance/demos/ps_matcher.html)),
-in which the probability of treatment assignment is modelled, but balance metrics are
-almost always anyway implicitly defining the success criterion. Our approach here is to
-explicitly define and directly optimize the balance metric that is relevant for the
-given problem.
+approach, in which the probability of treatment assignment is modelled, but balance metrics
+are almost always anyway implicitly defining the success criterion
+(see our [demo](https://bayer-group.github.io/pybalance/demos/ps_matcher.html)).
+Our approach here is to explicitly define and directly optimize the balance metric that
+is relevant for the given problem.
 
 The `pybalance` library implements several routines for optimizing balance. To learn more
 about these methods, head on over to the