Causality and Robust Optimization
This work addresses confounding bias in feature selection for decision-makers using machine learning, offering a novel approach that is incremental by building on existing methods and assumptions.
The paper tackles the problem of confounding bias in feature selection for machine learning predictions by proposing a meta-algorithm that modifies existing feature selection algorithms to convert confounding bias into prediction variance, which is then managed using robust optimization to improve decision-making throughput, as demonstrated in experimental results.
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.