Maier et al. (2010) introduced the relational causal model (RCM) for representing and inferring causal relationships in relational data. A lifted representation, called abstract ground graph (AGG), plays a central role in reasoning with and learning of RCM. The correctness of the algorithm proposed by Maier et al. (2013a) for learning RCM from data relies on the soundness and completeness of AG...

Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors. In this work, which focuses on NLI task, we introduce methodology Faithfulness-through-Counterfactuals, first generates a counterfactual hypothesis based logical predicates expressed in explanation, then evaluates if prediction consistent with that l...

Many applications call for learning causal models from relational data. We investigate Relational Causal Models (RCM) under relational counterparts of adjacency-faithfulness and orientation-faithfulness, yielding a simple approach to identifying a subset of relational d-separation queries needed for determining the structure of an RCM using d-separation against an unrolled DAG representation of...

Serializability is a prominent correctness criterion for an interleaved execution of concurrent transactions. Serializability guarantees that the interleaved execution of concurrent transactions corresponds to some serial execution of the same transactions. Many important business applications, however, require the system to impose a partial serialization order between transactions pinned to a ...