@article{(Open Science Index):https://publications.waset.org/pdf/16101,
	  title     = {Kernel Matching versus Inverse Probability Weighting: A Comparative Study},
	  author    = { Andy Handouyahia and  Tony Haddad and  Frank Eaton},
	  country	= {},
	  institution	= {},
	  abstract     = {Recent quasi-experimental evaluation of the Canadian Active Labour Market Policies (ALMP) by Human Resources and Skills Development Canada (HRSDC) has provided an opportunity to examine alternative methods to estimating the incremental effects of Employment Benefits and Support Measures (EBSMs) on program participants. The focus of this paper is to assess the efficiency and robustness of inverse probability weighting (IPW) relative to kernel matching (KM) in the estimation of program effects. To accomplish this objective, the authors compare pairs of 1,080 estimates, along with their associated standard errors, to assess which type of estimate is generally more efficient and robust. In the interest of practicality, the authorsalso document the computationaltime it took to produce the IPW and KM estimates, respectively.
},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {7},
	  number    = {8},
	  year      = {2013},
	  pages     = {1218 - 1233},
	  ee        = {https://publications.waset.org/pdf/16101},
	  url   	= {https://publications.waset.org/vol/80},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 80, 2013},
	}