New Method for Estimating Causal Effects in Partial Directed Acyclic Graphs
Researchers have developed a collapsibility method for estimating causal effects in completed partially directed acyclic graphs (CPDAGs) that maintains consistency when marginalizing over variables. The approach characterizes minimal collapsible sets as strong d-convex hulls and combines graph reduction with the IDA framework. This work advances causal inference methodology by enabling more reliable effect estimation in complex graphical models.
A new paper on arXiv proposes a method for estimating causal effects that preserves estimator consistency before and after marginalizing over certain variables in CPDAGs. The researchers introduce the concept of estimate collapsibility for CPDAGs and characterize the minimal collapsible sets as strong d-convex hulls. They develop an efficient algorithm to identify such sets in directed acyclic graphs (DAGs) and extend it to CPDAGs. The method combines a graph reduction procedure with the IDA (Intervention Do-calculus Algorithm) framework. Experimental results and empirical analysis demonstrate the effectiveness of the collapsibility approach for causal estimations in CPDAGs, with code made publicly available.
What different sources said
- arXiv cs.LGCenter
Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls
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