Deep causal learning for robotic intelligence

HIGHLIGHTS

• who: Yangming Li from the Fondazione Politecnico di Milano, Italy Qingdao University, China have published the paper: Deep causal learning for robotic intelligence, in the Journal: (JOURNAL)
• what: Work, Hassanpour and Greiner (2019a) proposed a context-aware importance sampling reweighting scheme to estimate ITEs, which addresses the distributional shift between the source (outcome of the administered treatment appearing in the observed training data) and target (outcome of the alternative treatment) that exists due to selection bias.
• how: The author declares that the research was conducted in the absence of any commercial or financial . . .
  

   

  

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