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The notion of actual causation, as formalized by Halpern and Pearl, has beenrecently applied to relational databases, to characterize and compute actualcauses for possibly unexpected answers to monotone queries. Causes take theform of database tuples, and can be ranked according to their causalresponsibility, a numerical measure of their relevance as a cause to the queryanswer. In this work we revisit this notion, introducing and making a case foran alternative measure of causal contribution, that of causal effect. Themeasure generalizes actual causes, and can be applied beyond monotone queries.We show that causal effect provides intuitive and intended results.

Quantifying Causal Effects on Query Answering in Databases