Introduction
Education is a crucial factor in the development of a thriving and prosperous society. From increasing economic growth to promoting civic engagement, education continues to be the key determinant of success in various aspects of life, especially career prospects and high-status income
levels[3]. Therefore, We intend to estimate the Conditional Average Treatment Effect (CATE) of
education level on earning using the “honest” estimator proposed in the paper Comprehension
and Reproduction of Recursive Partitioning for Heterogeneous Causal Effects, written by Susan
Athey and Guido Imbens[1]. Cate is designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies.
The authors created and benchmarked the unbiased estimator of CATE across subsets of the population with different treatments, proposing an “honest” approach
for estimation.
Research Problems
1. The CATE of college education on yearly income for different years, being 2010, 2000, and 1990.
2. The CATE of college education on yearly income for males and females in 2010.
3. The CATE of college education on yearly income for different age groups in 2010, being
people in their 30s, 40s, and 50s.
Learn More about our project
[1] Susan Athey and Guido Imbens. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of
Sciences, 113(27):7353-7360, jul 2016.
[2] Claude E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27(3):379-423, 1948.
[3] Ulrich Teichler. Higher Education and the World of Work: Conceptual Frameworks, Comparative Perspectives, Empirical Findings. Sense
Publishers, 2009.