论文标题
使用因果机学习分析非对称飞镖竞赛中的内置优势
Analysing a built-in advantage in asymmetric darts contests using causal machine learning
论文作者
论文摘要
我们与两名球员分析了一场连续比赛，其中一位参赛者享有技术优势。使用因果机学习文献中的方法，我们分析了内置优势，这是首先具有更多但从未减少的动作。我们的经验发现表明，第一迁移的可能性高8.6个百分点，以赢得技术优势引起的比赛。具有低表现措施且经验很少的参赛者具有最高的内置优势。关于公平原则，具有同等能力的参赛者应该具有同等的获胜概率，因此，对于竞争对手和随机的开始权利的同等内置优势，这项比赛是前公平的。尽管如此，比赛设计仍为同样熟练的参赛者赢得胜利的概率不平等，因为与社会压力相关的内置优势是在国内外竞争的参赛者。
We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6 percentage points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.
