HDOSE Strategic Reinvestment 项目 A Comparison of Machine Learning Methods to Detect the Causal Heterogeneous Treatment Effects for Multilevel Randomized Controlled Trials 支持因果推断与机器学习交叉领域的方法研究。
在实验室中，该项目关注使用机器学习研究因果异质性：不仅研究干预是否有效，也研究对谁有效、在什么条件下有效。
项目信息
- 资助来源： University of Florida HDOSE Strategic Reinvestment Fund
- 角色： 主持人
- Co-PI： Walter Leite
- 项目周期： 2022 年 5 月 1 日-2023 年 4 月 30 日
- 资助金额： $12,500
项目重点
- 比较用于检测异质性处理效应的机器学习方法。
- 处理多层随机对照试验情境。
- 为教育中的因果异质性分析提供方法指导。
相关论文和工作论文
- Li, W., Gao, X., Ren, S., & Dong, N. (2026). Heterogeneous treatment effects for impact evaluations. Manuscript under review.
- Konstantopoulos, S., Li, W., Miller, S., & van der Ploeg, A. (2019). Using quantile regression to estimate intervention effects beyond the mean. Educational and Psychological Measurement, 79(5), 883-910. doi:10.1177/0013164419837321
- Strickland, K. J., Hill, J., & Li, W. Estimating heterogeneous treatment effects of the gifted and talented program using Bayesian additive regression trees. Working paper.
相关报告
- Strickland, K. J., Hill, J., Lu, Y., & Li, W. (2026). Estimating heterogeneous effects of the gifted and talented program using Bayesian additive regression trees. Modern Modeling Methods Conference.
- Li, W., Leite, W., & Quan, J. (2024). Application of machine learning algorithms to detect treatment effect heterogeneity for three-level multisite experiments. AERA Annual Meeting.
- Li, W. (2024). Using Machine Learning Methods to Detect Heterogeneous Treatment Effects for Multilevel Studies in Education. UF Education Policy Brown Bag.