Probabilistic Variational Causal Effect as A new Theory for Causal
Reasoning
- URL: http://arxiv.org/abs/2208.06269v1
- Date: Fri, 12 Aug 2022 13:34:17 GMT
- Title: Probabilistic Variational Causal Effect as A new Theory for Causal
Reasoning
- Authors: Usef Faghihi, Amir Saki
- Abstract summary: We introduce a new causal framework capable of dealing with probabilistic and non-probabilistic problems.
Our formula of causal effect uses the idea of total variation of a function integrated with probability theory.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, we introduce a new causal framework capable of dealing with
probabilistic and non-probabilistic problems. Indeed, we provide a formula
called Probabilistic vAriational Causal Effect (PACE). Our formula of causal
effect uses the idea of total variation of a function integrated with
probability theory. PACE has a parameter $d$ determining the degree of being
probabilistic. The lower values of $d$ refer to the scenarios that rare cases
are important. In contrast, with the higher values of $d$, our model deals with
the problems that are in nature probabilistic. Hence, instead of a single value
for causal effect, we provide a causal effect vector by discretizing $d$. We
also address the problem of computing counterfactuals in causal reasoning. We
compare our model to the Pearl model, the mutual information model, the
conditional mutual information model, and the Janzing et al. model by
investigating several examples.
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