在传统教育学定量研究中，回归模型及其参数估计常用于分析相关关系。然而，在处理大规模数据中的处理效应异质性时，这种参数模型的拟合效果不尽理想。特别是模型中交互项的设置带有较强的主观性，使得其泛化能力有限。随着机器学习理论的发展，集成树模型等机器学习方法在处理大规模数据方面显示出优异的拟合效果。以数据驱动的研究范式相较于传统模型驱动的方法展现出更佳的研究特性，机器学习在因果关系研究中的应用呈现出巨大潜力。

本研究运用因果森林模型对教育测量领域中处理效应异质性问题进行深入分析。以课时投入时间作为处理变量，研究了不同混淆变量对课时投入与科学素养影响问题中的异质性，即课堂效率的影响。研究发现：首先，在个体层面，工具性动机的合理运用能有效提高课时投入效果，同时，增强学生科学兴趣对提升课时投入的边际效益同样重要。其次，在家庭层面，低家庭收入背景下的学生通过课时投入获得的科学素养边际效益更为显著，显示出经济条件较弱家庭学生能从课堂学习中获得更多益处。家庭教育资源的缺乏同样会增强课时投入的边际效益，父母职业地位较低的学生也能通过课堂学习获得更大的边际收益。最后，在学校层面，适中的课堂纪律环境有利于提高课时投入的积极效果，而过高的纪律水平可能会抑制课时投入的边际效益；良好的教师指导适配性显著增强了课时投入对科学素养的正向影响；在科学教学资源较为丰富的环境中，课时投入的边际效益呈下降趋势；适当的高级教师比例能够进一步提升课时投入的边际效益。

本研究基于相关数据分析，针对当前“双减”政策背景下，如何提高课堂效率以及促进教育公平提出以下政策建议：首先，应合理控制课堂纪律，提升学习效率；其次，优化资源配置，提高教学质量；再次，激发学生对科学的兴趣，提高课堂学习效果，培养拔尖创新人才；最后，提供社会化辅导，以促进教育公平。

In traditional educational quantitative research, regression models and their parameter estimations are commonly utilized to analyze correlational relationships. However, these parametric models exhibit suboptimal fitting effects when addressing heterogeneity in treatment effects within large-scale datasets. Particularly, the subjectivity inherent in setting interaction terms in these models limits their generalizability. With the evolution of machine learning theories, methods like ensemble tree models have demonstrated superior fitting capabilities for large-scale data. Data-driven research paradigms, as opposed to traditional model-driven approaches, show more favorable characteristics, with machine learning applications in causal research revealing substantial potential.

This study employs the causal forest model to conduct an in-depth analysis of heterogeneity in treatment effects within the field of educational measurement. Using class time investment as the treatment variable, the study explores heterogeneity in the impact of class time investment on scientific literacy, focusing on classroom efficiency across various confounding variables. The findings indicate that, at the individual level, students with high self-efficacy gain greater learning benefits from class time investment. Additionally, the rational use of instrumental motivation enhances the effectiveness of class time investment, while fostering students' interest in science also significantly boosts the marginal benefits of class time investment. At the family level, students from lower-income backgrounds exhibit more pronounced marginal benefits in scientific literacy from class time investment, suggesting that students from economically disadvantaged families benefit more from classroom learning. A lack of family educational resources similarly enhances the marginal benefits of class time investment, and students with parents of lower occupational status also gain larger marginal benefits from classroom learning. At the school level, a moderate classroom discipline environment is conducive to positive outcomes of class time investment, but excessively strict discipline may inhibit its marginal benefits. Effective teacher guidance adaptability significantly enhances the treatment effect of class time on scientific literacy. Moreover, in environments with abundant scientific teaching resources, the marginal benefits of class time investment decrease. Finally, an appropriate proportion of advanced teachers further enhances the marginal benefits of class time investment.

Based on the data analysis, this study offers policy recommendations in the context of the current "Double Reduction" policy, aiming to reduce class time, enhance classroom efficiency, and promote educational equity: First, reasonably control classroom discipline to improve the efficiency of science learning. Second, optimize the allocation of teaching resources to enhance teaching quality. Third, boost students' scientific self-efficacy to cultivate top innovative talents. Furthermore, stimulate students' interest in science to improve classroom learning outcomes. Finally, provide socialized tutoring to promote educational equity.