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Activity Number:
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299
- Machine Learning in Causal Inference with Applications in Complicated Settings
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Biometrics Section
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Abstract #312272
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Title:
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DOES the MARKOV DECISION PROCESS FIT the DATA: TESTING for the MARKOV PROPERTY in SEQUENTIAL DECISION MAKING
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Author(s):
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Chengchun Shi* and Runzhe Wan and Rui Song and Wenbin Lu and Ling Leng
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Companies:
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The London School of Economics and NC State Univeristy and NC State University and North Carolina State University and Amazon
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Keywords:
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reinforcement learning;
sequential decision making;
Markov decision process;
forward-backward learning
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Abstract:
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The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this talk, we propose a novel Forward-Backward Learning procedure to test MA in sequential decision making. The proposed test does not assume any parametric form on the joint distribution of the observed data and plays an important role for identifying the optimal policy in high-order Markov decision processes and partially observable MDPs. We apply our test to both synthetic datasets and a real data example from mobile health studies to illustrate its usefulness.
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Authors who are presenting talks have a * after their name.
