Abstract

Abstract: This study investigates the presence and extent of discrimination against Asian American applicants in the college admissions process, particularly in the context of the Harvard University legal case. Using machine learning techniques, we develop an analytical framework to examine publicly available data from judicial documents. Our approach involves creating a two-stage model where we first estimate an auxiliary model to capture the endogenous variablestudent performanceand then incorporate these predictions into the primary admissions outcome model. By generating counterfactual scenarios where applicants' racial backgrounds are hypothetically altered, we aim to identify the causal impact of being Asian American on admission outcomes. Our findings indicate that while there is no significant bias against Asian American applicants in academic and extracurricular ratings, strong disparities exist in non-academic ratings, such as personal evaluations. If Asian American applicants received personal ratings comparable to White applicants, their admission rates could increase by approximately 27%, particularly for those with higher academic performance. These results suggest potential biases in the admissions process, warranting further investigation.

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