> The SHAP implementation is used to estimate the shapley vectors which FACET then decomposes into synergy, redundancy, and independence vectors.
But FACET it still about sorting things out in the 'correlation world'.
To get back to SURD: IMHO, when talking about causality one should incorporate some kind of precedence, or order; One thing is the cause of another. Here in SURD they sort of introduce it in a roundabout way by using time's order:
> requiring only pairs of past and future events for analysis
But maybe we could have had fully-fledged custom DAGs, like from here https://github.com/nathanwang000/Shapley-Flow (which don't yet have the redundant/unique/synergistic decomposition)
Also, how do we deal with undetectable "post hoc ergo propter hoc" fallacy, though? (travesting time as causal ordering). How do we deal with confounding? Custom DAGs would have been great.
I'm longing for a SURD/SHAP/FACET/Shapleyflow integration paper. We're so close to it.
Seen elsewhere: https://github.com/BCG-X-Official/facet, which uses SHAP attributions as inputs:
> The SHAP implementation is used to estimate the shapley vectors which FACET then decomposes into synergy, redundancy, and independence vectors.
But FACET it still about sorting things out in the 'correlation world'.
To get back to SURD: IMHO, when talking about causality one should incorporate some kind of precedence, or order; One thing is the cause of another. Here in SURD they sort of introduce it in a roundabout way by using time's order:
> requiring only pairs of past and future events for analysis
But maybe we could have had fully-fledged custom DAGs, like from here https://github.com/nathanwang000/Shapley-Flow (which don't yet have the redundant/unique/synergistic decomposition)
Also, how do we deal with undetectable "post hoc ergo propter hoc" fallacy, though? (travesting time as causal ordering). How do we deal with confounding? Custom DAGs would have been great.
I'm longing for a SURD/SHAP/FACET/Shapleyflow integration paper. We're so close to it.