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Unexpected model behaviors need explanation, so valid behaviors can be separated from errors. Understanding unexpected behavior requires accumulation of insight into the behavior and the conditions under which it arises. Explanation exploration (EE) has been presented to gather insight into unexpected behaviors. EE provides subject matter experts (SMEs) with the capability to test hypotheses about an unexpected behavior by semi-automatically creating conditions of interest under which SMEs can observe the unexpected behavior. EE also reveals the interactions of identified variables that influence the unexpected behavior. Causal program slicing, improves EE by: automatically identifying all variables in the model that may influence the unexpected behavior, quantifying how the state changes in those variables influence the unexpected behavior, and mapping the quantified state changes in the variables to the statements in the modelpsilas source code that cause change in state. These capabilities require less SME knowledge and provide more insight than EE.