The impact of early childhood education and medical care on children
DOI:
https://doi.org/10.61173/7dqx4j86Keywords:
ihdp, cate, bart, metalearnersAbstract
Causal inference is a statistical approach that aims to understand and quantify the causal relationship between variables, allowing us to determine the impact of one variable on another while accounting for potential confounding factors. In this study, we chose the Infant Health and Development Program (IHDP) dataset to test whether high-quality early childhood education and medical care will enhance their cognitive and academic ability. We use some common and classic algorithms to achieve this study by calculating the CATE (Conditional Average Treatment Effect) of the dataset; if the result is a negative number, that means early treatment doesn’t improve children’s further ability; if the result is a positive number, that means treatment improves it. After fitting the targeted models, BART(Bayesian Additive Regression Trees) and Metalearners, into this dataset, we found out that all the models will get positive CATE, which means that these early treatments positively affect children’s future development. Also, we can judge which model gives us a more accurate result according to the standard variance of TE(treatment effect). The result of this study will provide us with an insightful idea about whether we should give children some treatments regarding education and medical care and which model is more suitable for this casual inference test.
References
[1] Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics,
[20] 1, 217-240.
[2] Athey, S., & Imbens, G. W. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113.27, 7353-7360.
[3] Hill, J. L., & Su, Y. S. (2011). Assessing differential treatment effects with tree-based models: Evidence from heart attack patients. Political Analysis, 19.4, 385-408.
[4] Wager, S., Athey, S., & Imbens, G. W. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113.523, 1228-1242.
[5] Linero, A. R., & Pérez-Stable, E. J. (2018). Bayesian hierarchical model for causal inference in high-dimensional longitudinal studies. Journal of the American Statistical Association, 113.523, 646-659.
[6] Ibrahim, J. G., Chen, M. H., Sinisi, S. E., & Kim, Y. (2019). Bayesian methods for estimating causal mediation effects using principal stratification with dichotomous mediators. Journal of the American Statistical Association, 114.528, 812-824.
[7] Kern, C., Wagner, S., & Schmid, V. J. (2019). Identifying causal interactions in a high-dimensional Bayesian additive regression tree framework. Journal of Computational and Graphical Statistics, 28.1, 180-192.
[8] Koshiaris, C., & Car, J. (2021). Antidepressants for depressive symptoms in patients with comorbid physical illness: a systematic review and meta-analysis. Journal of General Internal Medicine, 36.1, 260-267.
[9] Huang, W., & Aljadeff-Abergel, E. (2021). The effect of a math intervention program on elementary students’ math achievement: Evidence from a randomized controlled trial. Journal of Educational Psychology, 113.2, 201-215.
[10] Ibrahim, Joseph G. Ming‐Hui Chen, and Debajyoti Sinha.
[2009] . Computational Statistics 1.2. In: Wiley Interdisciplinary Reviews, WB, Bayesian Survival Analysis. 152-159.
[11] Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. In: Proceedings of the National Academy of Sciences, 116.10, 4163.
[12] Künzel, Sören R. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning.In: Proceedings of the National Academy of Sciences 116.10: 4156- 4165.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
