  In the realm of competitive sports, understanding the performance dynamics of
athletes, represented by the age curve (showing progression, peak, and
decline), is vital. Our research introduces a novel framework for quantifying
age-specific treatment effects, enhancing the granularity of performance
trajectory analysis. Firstly, we propose a methodology for estimating the age
curve using game-level data, diverging from traditional season-level data
approaches, and tackling its inherent complexities with a meta-learner
framework that leverages advanced machine learning models. This approach
uncovers intricate non-linear patterns missed by existing methods. Secondly,
our framework enables the identification of causal effects, allowing for a
detailed examination of age curves under various conditions. By defining the
Age-Conditioned Treatment Effect (ACTE), we facilitate the exploration of
causal relationships regarding treatment impacts at specific ages. Finally,
applying this methodology to study the effects of rest days on performance
metrics, particularly across different ages, offers valuable insights into load
management strategies' effectiveness. Our findings underscore the importance of
tailored rest periods, highlighting their positive impact on athlete
performance and suggesting a reevaluation of current management practices for
optimizing athlete performance.
