On Measuring Influence in Avoiding Undesired Future

When a predictive model anticipates an undesired future event, a question arises: what can we do to avoid it? Resolving this forward-looking challenge requires determining the variables that positively influence the future, moving beyond statistical correlations typically exploited for prediction. In this paper, we introduce a novel framework for evaluating the influence of actionable variables in successfully avoiding the undesired future. We quantify influence as the degree to which the probability of success can be increased by altering variables based on the principle of maximum expected utility. While closely related to causal effects, our analysis reveals a counterintuitive insight: influential variables may not necessarily be those with intrinsically strong causal effects on the target. In fact, due to the dynamics of the decision process, it can be highly beneficial to alter a weak causal factor, or even a variable that is not an intrinsic factor at all. We provide a practical implementation for computing the proposed quantity using observational data and demonstrate its utility through empirical studies on synthetic and real-world applications.