Title: A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform,
Abstract: Kiva is an online non-profit crowdsouring microfinance platform that raises
funds for the poor in the third world. The borrowers on Kiva are small business
owners and individuals in urgent need of money. To raise funds as fast as
possible, they have the option to form groups and post loan requests in the
name of their groups. While it is generally believed that group loans pose less
risk for investors than individual loans do, we study whether this is the case
in a philanthropic online marketplace. In particular, we measure the effect of
group loans on funding time while controlling for the loan sizes and other
factors. Because loan descriptions (in the form of texts) play an important
role in lenders' decision process on Kiva, we make use of this information
through deep learning in natural language processing. In this aspect, this is
the first paper that uses one of the most advanced deep learning techniques to
deal with unstructured data in a way that can take advantage of its superior
prediction power to answer causal questions. We find that on average, forming
group loans speeds up the funding time by about 3.3 days.