Addressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.