传统的教育学定量研究往往围绕回归模型展开，以有关参数的估计来分析相关关系，对于处理效应异质性的表达以交互项的形式开展，在维数和数量都较大的大数据中，这种参数模型对于大样本数据的拟合效果较差，且模型的交互项设置具有极强主观性，泛化能力较弱。随着机器学习理论的发展，以集成树模型为代表的机器学习方法展现出了对大数据的优秀拟合效果，以数据为驱动的研究范式展现出比以模型为驱动的传统研究范式更加良好的性质，借助机器学习对因果关系展开研究成为极具潜力的发展方向。

本研究利用因果森林对教育测量领域的处理效应异质性问题进行分析，以科学学习时间作为处理变量，研究了不同混淆变量在科学学习时间对科学素养影响问题中的异质性，即学习效率的影响问题，并得出了以下结论。第一，在PISA2015的芬兰和日本样本中，家庭经济社会文化地位展现出了对该处理效应异质性问题存在的潜在支配问题，处理效应值随着家庭经济社会文化地位指数的升高出现了逐渐增大的趋势，第二，指出了探究性教学对该处理效应异质性问题有较大的特征重要性，但异质性的来源为该处理效应值具有较大的波动，第三，发现了认知变量在该处理效应异质性问题上的差异化表现，在芬兰样本中，提高科学自我效能可以有效提高处理效应值，而广义科学兴趣的增加却导致了处理效应值的下降，而在日本样本中，科学自我效能的提高会导致处理效应的下降，而广义科学兴趣的提高会导致处理效应的先增高后降低。第四，本文还发现了日本样本中母亲教育水平对该问题的较大影响，说明了将教育更多放归家庭可能存在的潜在影响。第五，本文以性别为混淆变量发现了在芬兰、中国四省市、加拿大样本中都存在的女性样本比男性样本更大的处理效应，说明了女性在科学素养提升方面的更大潜力，纠正了普遍存在的性别偏见问题。

本研究利用研究的相关数据，为“双减”政策下降低科学学习时间、提高学习效率、促进教育公平提供了相关政策建议。第一，注意在“双减”实行过程中可能会出现的教育不公平现象，第二，不过分依赖特定教学方式，努力提高课堂教学质量，第三，优化高科学自我效能学生的培养机制，充分发挥高科学自我效能人才存在的高效学习潜力，第四，提供社会化辅导工作，为无法在家庭教育中得到良好帮助的学生提供支持，第五，改善校园舆论，纠正性别偏见，发挥女性在高科学素养人才培养中的重要作用。

Traditional quantitative research on pedagogy tends to focus on regression models, analyze correlation with the estimation of relevant parameters, and carry out the expression of effect heterogeneity in the form of interactive terms. In the big data with large dimensions and quantities, this parameter model has poor fitting effect on large sample data, and the setting of interactive terms of the model is highly subjective, and the generalization ability is weak. With the development of machine learning theory, the machine learning method represented by the integrated tree model has shown excellent fitting effect on big data, and the data-driven research paradigm has shown better properties than the model-driven traditional research paradigm. It has become a promising development direction to carry out research on causality with the help of machine learning. In this study, the causal forest was used to analyze heterogeneous treatment effect in the field of educational measurement.

With the science learning time as the treatment variable, the heterogeneity of different confounding variables in the influence of science learning time on science literacy was studied, that is, the influence of learning efficiency. this study mainly draws the following conclusions. First, in the Finland and Japan samples of PISA2015, family socioeconomic and cultural status presents a potential dominating problem on the heterogeneity of the treatment effect, and the treatment effect value gradually increases with the increase of family socioeconomic and cultural status index. Second, It is pointed out that inquiry teaching is of great characteristic importance to the heterogeneity of the treatment effect, but the source of the heterogeneity is that the effect value of the treatment has a great fluctuation. Thirdly, the differentiation of cognitive variables in the heterogeneous treatment effect is found. In the Finnish sample, the increase of scientific self-efficacy can effectively improve the processing effect value, while the increase of generalized scientific interest leads to the decrease of the processing effect value. In the Japanese sample, the increase of scientific self-efficacy leads to the decrease of the processing effect value. However, the increase of general scientific interest will lead to the first increase and then decrease of treatment effect. Fourthly, we also found that in the Japanese sample, the mother's level of education had a larger effect on the problem, indicating the potential impact of placing more education in the family. Fifth, correcting the widespread gender bias, taking gender as the confounding variable, this paper found that female samples in Finland, four provinces and cities in China and Canada had a larger treatment effect than male samples, indicating that women have greater potential in improving scientific literacy.

Using relevant data, this study provides relevant policy suggestions for reducing scientific learning time, improving learning efficiency and promoting educational equity under the "double reduction" policy. First, pay attention to the educational inequity that may occur during the implementation of "double reduction"; second, try to improve the quality of classroom teaching by not relying too much on specific teaching methods; third, optimize the training mechanism of students with high scientific self-efficacy and give full play to the efficient learning potential of talents with high scientific self-efficacy; fourth, provide social tutoring. Provide support for students who cannot get good help in home education. Fifth, improve public opinion on campus, correct gender bias, and give play to the important role of women in the cultivation of scientific talents.