A Divide and Conquer Approach for Solving Structural Causal Models
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2024Resumen
Structural causal models permit causal and counterfactual reasoning, and can be regarded
as an extension of Bayesian networks. The model consists of endogenous and exogenous
variables, with exogenous variables often being of unknown semantic interpretation. Consequently,
they are typically non-observable, with the result that counterfactual queries may
be unidentifiable. In this setting, standard inference algorithms for Bayesian networks are
insufficient. Recent methods attempt to bound unidentifiable queries through imprecise
estimation of exogenous probabilities. However, these approaches become unfeasible with
growing cardinality of the exogenous variables. This paper proposes a divide and conquer
method that transforms a general causal model into a set of models with low-cardinality
exogenous variables, for which any query can be calculated exactly. Bounds for a query in
the original model are then efficiently approximated by aggregating the results for the set
of small...
Palabra/s clave
Estadística ; Inteligencia Artificial