Causal inference from data. On some inadequacy problems of structures with hidden causes

O.S. Balabanov


The reliability of causal inference from data (by independence-based methods) is analyzed. We uncover some mechanisms which may result in model inadequacy due to sample bias and hidden variables. We detect some specific problems in recognition of direction of influence when some causes are hidden. Incorrectness of known rule for edge orientation (under causal insufficiency) is revealed. We suggest the correction to the rule aiming to retain model adequacy.

Problems in programming 2020; 2-3: 392-406


causal network; edge orientation; conditional independence; d-separation; collider; chain


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