amend repo

README.md

@@ -2,12 +2,12 @@
 
 This repo contains a Python implementation of _weighted doubly robust learning_, due to Zhan, et al. (2024). It is a method of uplift modeling to recover causal effects of treatments in the presence of treatment confounding. For instance, a company may issue multiple coupons to individual customers during an ad campaign. This method, borrowing from Shapley values, is designed to disentangle each treatment's effect.
 
+The two notebooks in the repo implement the algorithm, and demonstrate good performance on simulated data under different conditions.
+
 The diagram below captures the essentials elements of the algorithm:
 
 ![WDRL](wdrl.png)
 
-The two notebooks in the repo demonstrate good performance on simulated data under different conditions
-
 ## Reference
 
 Zhan, B., Liu, C., Li, Y. and Wu, C., 2024. Weighted doubly robust learning: An uplift modeling technique for estimating mixed treatments' effect. _Decision Support Systems_, 176, p.114060. https://doi.org/10.1016/j.dss.2023.114060