Why do companies choose particular capital structures? A compelling answer to this question remains elusive despite extensive research. In this article, we use double machine learning to examine the heterogeneous causal effect of credit ratings on leverage. Taking advantage of the flexibility of random forests within the double machine learning framework, we model the relationship between variables associated with leverage and credit ratings without imposing strong assumptions about their functional form. This approach also allows for data-driven variable selection from a large set of individual company characteristics, supporting valid causal inference. We report three findings: First, credit ratings causally affect the leverage ratio. Having a rating, as opposed to having none, increases leverage by approximately 7 to 9 percentage points, or 30\% to 40\% relative to the sample mean leverage. However, this result comes with an important caveat, captured in our second finding: the effect is highly heterogeneous and varies depending on the specific rating. For AAA and AA ratings, the effect is negative, reducing leverage by about 5 percentage points. For A and BBB ratings, the effect is approximately zero. From BB ratings onwards, the effect becomes positive, exceeding 10 percentage points. Third, contrary to what the second finding might imply at first glance, the change from no effect to a positive effect does not occur abruptly at the boundary between investment and speculative grade ratings. Rather, it is gradual, taking place across the granular rating notches ("+/-") within the BBB and BB categories.

翻译：公司为何选择特定的资本结构？尽管已有大量研究，但对此问题仍缺乏令人信服的答案。本文运用双重机器学习方法，考察信用评级对杠杆率的异质性因果效应。借助双重机器学习框架中随机森林的灵活性，我们对与杠杆率和信用评级相关的变量间关系进行建模，无需对其函数形式施加强假设。该方法还能从大量公司个体特征中实现数据驱动的变量选择，从而支持有效的因果推断。我们报告了三项发现：首先，信用评级对杠杆率存在因果性影响。与无评级相比，拥有评级会使杠杆率提高约7至9个百分点，相当于样本平均杠杆率的30%至40%。但这一结果存在重要限制条件，体现在我们的第二项发现中：该效应具有高度异质性，且随具体评级而变化。对于AAA和AA评级，效应为负向，使杠杆率降低约5个百分点；对于A和BBB评级，效应近似为零；从BB评级开始，效应转为正向且超过10个百分点。第三，与第二项发现可能带来的初步印象相反，从无效应到正向效应的转变并非突然发生在投资级与投机级评级的边界。相反，这一转变是渐进的，发生在BBB和BB类别内部细粒度评级微调（"+/-"）的过程中。