Can Causal (or Counterfactual) Representations benefit from Quantum Computing?

Causal questions have often permeated through our daily lives in different industries such as Healthcare and Legal system. Causality refers to the study of causes and effects. Causality first emerged from the field of philosophy and has now even spread in common machine learning practices to adopt prior knowledge between different features Pearl [2009]. Although there have been papers on causal estimation and effects for quantum computing Barrett et al. [2019], in this abstract we hope to spark discussions around the opposing case, that is, how latent causal representations can possibly be modelled through quantum computing.