Causal Reasoning in Construction Process Scheduling

Abstract

This paper introduces an advanced framework for modeling and scheduling construction processes using causal inference techniques, with particular emphasis on capturing complex technological and organizational interdependencies. By integrating causal calculus and counterfactual reasoning, the study demonstrates how construction schedules can be analyzed and optimized not only through temporal relationships but also through explicit cause–effect structures. A matrix-based scheduling methodology is presented, incorporating diagonal and reverse-diagonal time couplings consistent with the Time Coupling Method (TCM). The computational procedure is detailed, including the determination of earliest and latest event times, identification of the critical path, and computation of activity floats. Based on an in-depth examination of technological and organizational constraints, eight theorems are formulated and proven, establishing the fundamental properties of a scheduling approach that embeds causal mechanisms. The findings indicate that the integration of causal inference into construction planning enables more accurate identification of factors influencing project duration, enhances synchronization of dependent activities, and minimizes conflicts and idle times. This causally informed framework strengthens decision-making by allowing practitioners to predict the consequences of modifications in project execution strategies. The developed models constitute a robust foundation for future research on leveraging causal inference algorithms and artificial intelligence to advance construction process management.