Causal Inference Methods and their Challenges: The Case of 311 Data Conference

Yusuf, Farzana Beente, Cheng, Shaoming, Ganapati, Sukumar et al. (2021). Causal Inference Methods and their Challenges: The Case of 311 Data . 49-59. 10.1145/3463677.3463717

Open Access

cited authors

  • Yusuf, Farzana Beente; Cheng, Shaoming; Ganapati, Sukumar; Narasimhan, Giri

date/time interval

  • June 9, 2021 -

publication date

  • January 1, 2021

keywords

  • 311 customer service
  • BAYESIAN NETWORKS
  • Bayesian network
  • CITIZEN
  • Causal networks
  • Computer Science
  • Computer Science, Artificial Intelligence
  • Computer Science, Interdisciplinary Applications
  • Computer Science, Theory & Methods
  • GOVERNMENT
  • Government & Law
  • Political Science
  • Public Administration
  • RISK-ASSESSMENT
  • SERVICES
  • Science & Technology
  • Social Sciences
  • Technology

Location

  • ELECTR NETWORK, Univ Nebraska Omaha, Coll Publ Affairs & Community Serv, Ctr Publ Affairs

Digital Object Identifier (DOI)

Conference

  • 22nd Annual International Conference on Digital Government Research (DGO) - Digital Innovations for Public Values - Inclusive Collaboration and Community

publisher

  • ASSOC COMPUTING MACHINERY

start page

  • 49

end page

  • 59