A while ago I blogged about Facebook’s causal inference group. Now Microsoft has followed suit and released a Python library for graph-based methods of causal inference.
“For decades, causal inference methods have found wide applicability in the social and biomedical sciences. As computing systems start intervening in our work and daily lives, questions of cause-and-effect are gaining importance in computer science as well. To enable widespread use of causal inference, we are pleased to announce a new software library, DoWhy. Its name is inspired by Judea Pearl’s do-calculus for causal inference. In addition to providing a programmatic interface for popular causal inference methods, DoWhy is designed to highlight the critical but often neglected assumptions underlying causal inference analyses.”
Thanks to Matt Ranger (@vhranger) for the pointer!
At the moment the library’s functionality is still limited (mostly to backdoor adjustment and covariate stratification). But the team seems to be committed to extending its scope in the near future.
“In the future, we look forward to adding more features to the library, including support for more estimation and sensitivity methods and interoperability with available estimation software. We welcome your feedback and contributions as we develop the library. You can check out the DoWhy Python library on Github. We include a couple of examples to get you started through Jupyter notebooks here.”
Great to see that causal inference—once a purely academic endeavor–finds more and more applications in business and that leading tech firms invest in these capabilities. Slowly, it’s becoming one of the hottest topics in data science right now. So don’t miss out!