  Recent years, large scale clinical data like patient surveys and medical
record data are playing an increasing role in medical data science. These
large-scale clinical data, collectively referred to as "real-world data (RWD)".
It is expected to be widely used in large-scale observational studies of
specific diseases, personal medicine or precise medicine, finding the responder
of drugs or treatments. Applying RWD for estimating heterogeneous treat ment
effect (HTE) has already been a trending topic. HTE has the potential to
considerably impact the development of precision medicine by helping doctors
make more informed precise treatment decisions and provide more personalized
medical care. The statistical models used to estimate HTE is called treatment
effect models. Powers et al. proposed a some treatment effect models for
observational study, where they pointed out that the bagging causal MARS (BCM)
performs outstanding compared to other models. While BCM has excellent
performance, it still has room for improvement. In this paper, we proposed a
new treatment effect model called shrinkage causal bagging MARS method to
improve their shared basis conditional mean regression framework based on the
following points: first, we estimated basis functions using transformed
outcome, then applied the group LASSO method to optimize the model and estimate
parameters. Besides, we are focusing on pursing better interpretability of
model to improve the ethical acceptance. We designed simulations to verify the
performance of our proposed method and our proposed method superior in mean
square error and bias in most simulation settings. Also we applied it to real
data set ACTG 175 to verify its usability, where our results are supported by
previous studies.
