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Workshop: Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice

Practical Policy Optimization with PersonalizedExperimentation

Mia Garrard · Hanson Wang · Ben Letham · Zehui Wang · Yin Huang · Yichun Hu · Chad Zhou · Norm Zhou · Eytan Bakshy


Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment [3,25]. However, standard experimentation platforms do not perform optimally for end user populations that exhibit heterogeneous treatment effects (HTEs) [3,5]. Here we present a personalized experimentation framework, Personalized Experiments (PEX), which optimizes treatment group assignment at the user level via HTE modeling and sequential decision policy optimization to optimize multiple short term and long term outcomes simultaneously. We describe an end-to-end workflow that has proven to be successful in practice and can be readily implemented using open-source software

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