Machine Learning in Causal Inference
Yiwei Shen · 2024
Causal inference enables us to move beyond merely observing correlations in understanding the actual causal relationships between variables, but how to connect it with machine learning model still needs careful and scientific study to judge. This paper discusses various methods for estimating causal effects and their application in different scientific implementation. The feasibility of these methods is explored through data of a social research, Early Childhood Longitudinal Study (ECLS), illustrating findings out of traditional and machine learning procedure, to provide heterogeneous influence on estimating causal inference.