Scandinavian Working Papers in Economics
Working Paper Series,
IFAU - Institute for Evaluation of Labour Market and Education Policy
No 2004:9: Non-parametric adjustment for covariates when estimating a treatment effect
Eva Cantoni () and Xavier de Luna
Additional contact information
Eva Cantoni: Department of of Econometrics, University of Geneva, Postal: 40, Bd du Pont d'Arve, CH-1211 Geneva 4, Switzerland
Xavier de Luna: Department of Statistics, Umeå University, Postal: S-901 87 Umeå, Sweden
Abstract: We consider a non-parametric model for estimating the effect of a binary treatment on an outcome variable while adjusting for an observed covariate. A naive procedure consists in performing two separate non-parametric regression of the response on the covariate: one with the treated individuals and the other with the untreated. The treatment effect is then obtained by taking the difference between the two fitted regression functions. This paper proposes a backfitting algorithm which uses all the data for the two above-mentioned non-parametric regression. We give theoretical results showing that the resulting estimator of the treatment effect can have lower finite sample variance. This improvement may be achieved at the cost of a larger bias. However, in a simulation study we observe that mean squared error is lowest for the proposed backfitting estimator. When more than one covariate is observed our backfitting estimator can still be applied by using the propensity score (probability of being treated for a given setup of the covariates). We illustrate the use of the backfitting estimator in a several covariate situation with data on a training program for individuals having faced social and economic problems.
Keywords: Analysis of covariance; backfitting algorithm; linear smoothers; propensity score
JEL-codes: C14
20 pages, June 16, 2004
Full text files
wp04-09.pdf
- Published as
- Eva Cantoni and Xavier de Luna, (2006), 'Non-parametric adjustment for covariates when estimating a treatment effect', Journal of Nonparametric Statistics, vol 18, no 2, pages 227-244
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