Model averaging with causal effects for partially linear models

Treatment e ff ects with heterogeneity and heteroskedasticity are widely studied and applied in many fields, such as statistics and econometrics. The conditional average treatment e ff ect provides an excellent measure of the heterogeneous treatment e ff ect. In this paper, we propose a model averaging estimation for the conditional average treatment e ff ect with partially linear models based on the jackknife -type criterion under heteroscedastic error. Within this context, we provide theoretical justification for our model averaging approach, and we establish asymptotic optimality and weight convergence properties for our model under certain conditions. The performance of our proposed estimator is compared with that of classical estimators by using a Monte Carlo study and empirical analysis.