Minyus / causallift

CausalLift: Python package for causality-based Uplift Modeling in real-world business
https://causallift.readthedocs.io/
Other
338 stars 42 forks source link

Examples with Observational data #17

Closed XmataAdmin closed 3 years ago

XmataAdmin commented 4 years ago

Hello Yusuke, first of all, I wanted to say that you have done a great job with the package. There are two points that stand out to me and I see that frequently as well:

Minyus commented 4 years ago

Yes, I prepared a demo using observational data at: https://colab.research.google.com/github/Minyus/causallift/blob/master/notebooks/demo/CausalLift_demo.ipynb

In default, simulated observational dataset is used. To use an actual observational dataset called "Lalonde", set data = 'lalonde' instead of data = 'simulated_observational_data'.

Lalonde dataset was used to evaluate propensity score in the paper: Dehejia, R., & Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs. Journal of the American Statistical Association, 94(448), 1053-1062. doi:10.2307/2669919

mmubashir commented 4 years ago

Great. Thanks!