  In the standard data analysis framework, data is first collected (once for
all), and then data analysis is carried out. Moreover, the data-generating
process is typically assumed to be exogenous. This approach is natural when the
data analyst has no impact on how the data is generated. The advancement of
digital technology, however, has facilitated firms to learn from data and make
decisions at the same time. As these decisions generate new data, the data
analyst -- a business manager or an algorithm -- also becomes the data
generator. This interaction generates a new type of bias -- reinforcement bias
-- that exacerbates the endogeneity problem in static data analysis. Causal
inference techniques ought to be incorporated into reinforcement learning to
address such issues.
