Construction of cave mining systems requires significant capital expenditure and long-term commitment of human, equipment and material resources before production commences. Risks and uncertainties of these large-scale mining projects are significantly higher than for small-scale projects. This is due to the complexity and interactions of the construction schedule components and the large scale and uniqueness of such projects. One way to successfully manage construction uncertainties that would lead to schedule delays is to include a reasonable cost contingency in the budget estimation. A real options approach based on the least-squares Monte Carlo (LSM) method is used to estimate cost contingency associated with schedule uncertainty in mass mining projects. Cost contingency is estimated as the cost of potential project crashing (acceleration) decisions, which pays for not extending the schedule. While such a significant undertaking is expected to be subject to some delays, these can have an enormous impact on the economic value of such projects. The value of the crashing option was explicitly formalised into the process of valuation, as it is assumed to be a single option being considered in the valuation at every decision point prior to the construction completion date. As a part of the proposed method, a stochastic function for the percentage of remaining scope of work for the remaining duration of the entire project was integrated into a framework of numerical simulations. This ensured that this function can implicitly account for sources of schedule uncertainties for projects represented by multiple and overlapping work packages. The LSM method was applied to a real case scenario in an operating block cave mine, with modified scale and features. The results yielded by the LSM method indicated that significant changes in capital budget allocation stem from variation in a contractor's risk perception and confidence level of estimated delays.CITATION:Ahmed, H M and Dunbar, W S, 2016. Budget estimation and contingency allocation of block cave construction using least-squares Monte Carlo real options, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 801-808 (The Australasian Institute of Mining and Metallurgy: Melbourne).