Consistency of the non-parametric MTE approach with the random forest

Yuhan Fang

doi:10.1117/12.2648790

27 September 2022 Consistency of the non-parametric MTE approach with the random forest

Yuhan Fang

Proceedings Volume 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022); 123450M (2022) https://doi.org/10.1117/12.2648790
Event: 2022 International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 2022, Qingdao, China

Abstract

Heterogeneity is an important issue in the study of causal reasoning.It is shown by whether or not individuals receive treatment and how they respond to treatment differently. In contrast to the conventional treatment effect, which ignores the effect of heterogeneity, a marginal treatment effect (MTE) was introduced to represent the marginal benefit of treatment, which is heterogeneously dependent on observed and unobserved factors. However, traditional methods prefer the curse of dimensionality in calculating MTE, leading to bias in empirical studies. In view of this, this paper proposes a nonparametric framework based on machine learning algorithms and theoretically validates the consistency of the approach. In our framework, we first consistently generate propensity scores from random forests, and then apply the propensity scores to the classical identification of MTEs. The innovative nonparametric MTE approach in this paper shows reliable consistency in the estimation of high-dimensional causal inferences and allows for a more efficient assessment of policy interventions.

Citation Download Citation

Yuhan Fang "Consistency of the non-parametric MTE approach with the random forest", Proc. SPIE 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 123450M (27 September 2022); https://doi.org/10.1117/12.2648790

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