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A Complex Model of Consumer Food Acquisitions: Applying Machine Learning and Directed Acyclic Graphs to the National Household Food Acquisition and Purchase Survey (FoodAPS)

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  • Senia, Mark C.
  • Dharmasena, Senarath
  • Todd, Jessica E.

Abstract

Complex causal relationships among a large set of variables that affect the U.S. households’ food acquisition and purchase decisions were estimated using machine learning algorithms and directed acyclic graphs. Asians and Hispanics live in an environment with high concentrations of fast- and non-fast food restaurants. Obesity is less prevalent among Asians. Being Hispanic makes one to be more food insecure. Those with higher incomes are food secure and obesity is less prevalent among them. Being Black positively causes to be a SNAP participant and food insecure. Obesity is positively caused by fair/poor health and diet status.

Suggested Citation

  • Senia, Mark C. & Dharmasena, Senarath & Todd, Jessica E., 2018. "A Complex Model of Consumer Food Acquisitions: Applying Machine Learning and Directed Acyclic Graphs to the National Household Food Acquisition and Purchase Survey (FoodAPS)," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266536, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saea18:266536
    DOI: 10.22004/ag.econ.266536
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    References listed on IDEAS

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    Keywords

    Consumer/Household Economics; Food Consumption/Nutrition/Food Safety;

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