Estimating Categorical Counterfactuals via Deep Twin Networks. (arXiv:2109.01904v4 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2109.01904

Counterfactual inference is a powerful tool, capable of solving challenging
problems in high-profile sectors. To perform counterfactual inference, one
requires knowledge of the underlying causal mechanisms. However, causal
mechanisms cannot be uniquely determined from observations and interventions
alone. This raises the question of how to choose the causal mechanisms so that
resulting counterfactual inference is trustworthy in a given domain. This
question has been addressed in causal models with binary variables, but the
case of categorical variables remains unanswered. We …

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