  Deep neural networks excel at comprehending complex visual signals,
delivering on par or even superior performance to that of human experts.
However, ad-hoc visual explanations of model decisions often reveal an alarming
level of reliance on exploiting non-causal visual cues that strongly correlate
with the target label in training data. As such, deep neural nets suffer
compromised generalization to novel inputs collected from different sources,
and the reverse engineering of their decision rules offers limited
interpretability. To overcome these limitations, we present a novel contrastive
learning strategy called {\it Proactive Pseudo-Intervention} (PPI) that
leverages proactive interventions to guard against image features with no
causal relevance. We also devise a novel causally informed salience mapping
module to identify key image pixels to intervene, and show it greatly
facilitates model interpretability. To demonstrate the utility of our
proposals, we benchmark on both standard natural images and challenging medical
image datasets. PPI-enhanced models consistently deliver superior performance
relative to competing solutions, especially on out-of-domain predictions and
data integration from heterogeneous sources. Further, our causally trained
saliency maps are more succinct and meaningful relative to their non-causal
counterparts.
