Kavli Affiliate: Wei Gao
| First 5 Authors: Chong Ding, Zheng Li, Hon Keung Tony Ng, Wei Gao,
| Summary:
The treatment effect represents the average causal impact or outcome
difference between treatment and control groups. Treatment effects can be
estimated through social experiments, regression models, matching estimators,
and instrumental variables. In this paper, we introduce a novel
kernel-matching estimator for treatment effect estimation. This method is
particularly beneficial in observational studies where randomized control
trials are not feasible, as it uses the full sample to increase the
efficiency
and robustness of treatment effect estimates. We demonstrate that the
proposed estimator is consistent and asymptotically efficient under certain
conditions. Through Monte Carlo simulations, we show that the estimator
performs favorably against other estimators in the literature. Finally, we
apply our method to data from the National Supported Work Demonstration to
illustrate its practical application.
| Search Query: ArXiv Query: search_query=au:”Wei Gao”&id_list=&start=0&max_results=3