DingelNeiman-workathome VS EconML

Dr. Dingel writes - "Our code makes it easy for users to explore alternative assumptions about whether any given occupation can be done from home."

His repo - https://github.com/jdingel/DingelNeiman-workathome

Would be a worthwhile student project to replicate this in R/pandas (They used Stata on a Mac, if you know what I mean) & have an interactive online plot so one can change the survey assumptions & see what results. Just collating all this data in one place is a monumental effort.

I remember this paper was a "huge fucking deal" when it came out in Sep 2020. Has like ~1500 cites. Was used by Biden administration to set policy. Co-author Dr. Neiman was personally nominated by Biden for treasury. Authors are Booth school stalwarts. Pls do read the paper, very insightful even if you don't agree with its methodology/conclusions.

A good package with many of the tools used for this type of problem as well as pretty good documentation about how it all works is https://github.com/microsoft/EconML

Uplift Modeling (python): CausalML, EconML

Github: https://github.com/microsoft/EconML

Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.

EconML and CausalLift are pretty good python packages that help you build uplift models. scikit-uplift is a decent sklearn style wrapper package that can be helpful as well. One of the drawbacks of these packages is they only allow for the modeling of a single treatment. mr-uplift is a newer package that allows you to model the multiple treatment effects simultaneously. I haven't used it personally, but it does look fairly interesting.

Can't see how this adds to decades of causal inference packages development in stats oriented frameworks like R/Stata/EViews etc and the ongoing effort of porting this to Python. If you want something fancy there's already EconML.

Uplift Modeling - This is a very powerful technique aimed at discovering the customers who are most likely to respond to your marketing efforts. Some good python libraries for this are EconML and mr-uplift