  Motivated by neuroscientific and clinical applications, we empirically
examine whether observational measures of information flow can suggest
interventions. We do so by performing experiments on artificial neural networks
in the context of fairness in machine learning, where the goal is to induce
fairness in the system through interventions. Using our recently developed
$M$-information flow framework, we measure the flow of information about the
true label (responsible for accuracy, and hence desirable), and separately, the
flow of information about a protected attribute (responsible for bias, and
hence undesirable) on the edges of a trained neural network. We then compare
the flow magnitudes against the effect of intervening on those edges by
pruning. We show that pruning edges that carry larger information flows about
the protected attribute reduces bias at the output to a greater extent. This
demonstrates that $M$-information flow can meaningfully suggest targets for
interventions, answering the title's question in the affirmative. We also
evaluate bias-accuracy tradeoffs for different intervention strategies, to
analyze how one might use estimates of desirable and undesirable information
flows (here, accuracy and bias flows) to inform interventions that preserve the
former while reducing the latter.
