  Training models on data obtained from randomized experiments is ideal for
making good decisions. However, randomized experiments are often
time-consuming, expensive, risky, infeasible or unethical to perform, leaving
decision makers little choice but to rely on observational data collected under
historical policies when training models. This opens questions regarding not
only which decision-making policies would perform best in practice, but also
regarding the impact of different data collection protocols on the performance
of various policies trained on the data, or the robustness of policy
performance with respect to changes in problem characteristics such as action-
or reward- specific delays in observing outcomes. We aim to answer such
questions for the problem of optimizing sales channel allocations at LinkedIn,
where sales accounts (leads) need to be allocated to one of three channels,
with the goal of maximizing the number of successful conversions over a period
of time. A key problem feature constitutes the presence of stochastic delays in
observing allocation outcomes, whose distribution is both channel- and outcome-
dependent. We built a discrete-time simulation that can handle our problem
features and used it to evaluate: a) a historical rule-based policy; b) a
supervised machine learning policy (XGBoost); and c) multi-armed bandit (MAB)
policies, under different scenarios involving: i) data collection used for
training (observational vs randomized); ii) lead conversion scenarios; iii)
delay distributions. Our simulation results indicate that LinUCB, a simple MAB
policy, consistently outperforms the other policies, achieving a 18-47% lift
relative to a rule-based policy
