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Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn. (arXiv:2209.07749v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv case case study data linkedin optimization sales simulations study