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A trained neural network can be interpreted as a structural causal model(SCM) that provides the effect of changing input variables on the model'soutput. However, if training data contains both causal and correlationalrelationships, a model that optimizes prediction accuracy may not necessarilylearn the true causal relationships between input and output variables. On theother hand, expert users often have prior knowledge of the causal relationshipbetween certain input variables and output from domain knowledge. Therefore, wepropose a regularization method that aligns the learned causal effects of aneural network with domain priors, including both direct and total causaleffects. We show that this approach can generalize to different kinds of domainpriors, including monotonicity of causal effect of an input variable on outputor zero causal effect of a variable on output for purposes of fairness. Ourexperiments on twelve benchmark datasets show its utility in regularizing aneural network model to maintain desired causal effects, without compromisingon accuracy. Importantly, we also show that a model thus trained is robust andgets improved accuracy on noisy inputs.

Matching Learned Causal Effects of Neural Networks with Domain Priors