Operational decision-making with machine learning and causal inference

Optimizing operational decisions, routine actions within some business or operational process, is a key challenge across a variety of domains and application areas. The increasing availability of data, computational power, and advanced machine learning (ML) algorithms offers exciting opportunities for data-driven decision support. To advance the potential of ML for optimizing operational decision-making, we explore two research directions, aiming to develop ML models that are decision-focused and causal. This dissertation presents several developments in machine learning in these two areas. ML is effective at making predictions from historical data: for example, estimating a transaction's fraud probability by comparing it to past cases. However, decision-makers not only need to consider these predictions, but also the operational context. For example, the decision-maker uses predicted fraud probabilities to determine which transactions to investigate, while aiming to minimize monetary losses due to fraud and considering the available capacity of the fraud investigations team. Predictions can help reduce uncertainty (e.g., by predicting the fraud probability), but standard ML models are prediction-focused, instead of decision-focused. This distinction involves two challenges for data-driven decision-making. First, prediction-focused models prioritize predictive accuracy instead of the resulting decision quality (e.g., fraud losses recovered by the bank). Second, these models fail to account for operational constraints, such as the available investigation capacity. Decision-focused learning aims to improve data-driven decision-making by addressing these issues and incorporating the operational context into the optimization of ML models. In this dissertation, we analyze cost-sensitive learning within this prediction-optimization framework and evaluate general strategies for making cost-optimal decisions with ML. Additionally, we propose a novel ML method for optimal decision-making under capacity constraints based on learning to rank. To make effective decisions, a decision-maker has to estimate the causal effect of possible interventions in order to choose actions that achieve the desired outcome. Unfortunately, standard ML models identify correlations in the data instead of causal relationships. Because of this, these models cannot guarantee the effectiveness of decisions made based on their predictions. Causal inference provides a formal framework for reasoning about causality and identifying causal effects from data. This dissertation explores the intersection of causality and ML. First, we illustrate the potential of causal ML for optimizing preventive maintenance. Next, we propose novel causal ML methods for predicting causal effect distributions and for addressing informative sampling when predicting treatment outcomes over time. We also argue for a practical, end-to-end perspective for building ML pipelines for causal inference and propose an automated framework doing so. Finally, we combine decision-focused learning with causal inference by introducing ranking metalearners to optimize treatment decisions under capacity constraints.