  A decision-maker must consider cofounding bias when attempting to apply
machine learning prediction, and, while feature selection is widely recognized
as important process in data-analysis, it could cause cofounding bias. A causal
Bayesian network is a standard tool for describing causal relationships, and if
relationships are known, then adjustment criteria can determine with which
features cofounding bias disappears. A standard modification would thus utilize
causal discovery algorithms for preventing cofounding bias in feature
selection. Causal discovery algorithms, however, essentially rely on the
faithfulness assumption, which turn out to be easily violated in practical
feature selection settings. In this paper, we propose a meta-algorithm that can
remedy existing feature selection algorithms in terms of cofounding bias. Our
algorithm is induced from a novel adjustment criterion that requires rather
than faithfulness, an assumption which can be induced from another well-known
assumption of the causal sufficiency. We further prove that the features added
through our modification convert cofounding bias into prediction variance. With
the aid of existing robust optimization technologies that regularize risky
strategies with high variance, then, we are able to successfully improve the
throughput performance of decision-making optimization, as is shown in our
experimental results.
