Nonparametric Tests for Treatment Effect Heterogeneity ∗

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, i.e., that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs. JEL Classification: C14, C21, C52

[1]  Richard K. Crump,et al.  Dealing with limited overlap in estimation of average treatment effects , 2009 .

[2]  Xiaohong Chen,et al.  Semiparametric efficiency in GMM models with auxiliary data , 2007, 0705.0069.

[3]  G. Imbens,et al.  Mean-Squared-Error Calculations for Average Treatment Effects , 2005 .

[4]  Myoung‐jae Lee Micro-Econometrics for Policy, Program, and Treatment Effects , 2005 .

[5]  V. J. Hotz,et al.  Predicting the efficacy of future training programs using past experiences at other locations , 2005 .

[6]  Holger Dette,et al.  Nonparametric comparison of regression curves: An empirical process approach , 2003 .

[7]  Michael Lechner,et al.  Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods , 2002 .

[8]  Joel L. Horowitz,et al.  An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative , 2001 .

[9]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .

[10]  J. Angrist,et al.  Empirical Strategies in Labor Economics , 1998 .

[11]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator , 1998 .

[12]  Alberto Abadie,et al.  Instrumental Variables Estimation of Quantile Treatment Effects , 1998 .

[13]  J. Hahn On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects , 1998 .

[14]  W. Newey,et al.  Convergence rates and asymptotic normality for series estimators , 1997 .

[15]  O. Linton,et al.  Conditional Independence Restrictions: Testing and Estimation , 1996 .

[16]  Yongmiao Hong,et al.  Consistent Specification Testing via Nonparametric Series Regression , 1995 .

[17]  J. Riccio GAIN: Benefits, Costs, and Three-Year Impacts of a Welfare-to-Work Program. California's Greater Avenues for Independence Program. , 1994 .

[18]  E. Mammen,et al.  Comparing Nonparametric Versus Parametric Regression Fits , 1993 .

[19]  Herman J. Bierens,et al.  A consistent conditional moment test of functional form , 1990 .

[20]  P. Rosenbaum The Role of a Second Control Group in an Observational Study , 1987 .

[21]  H. Bierens Consistent model specification tests , 1982 .

[22]  E. Lehmann,et al.  Nonparametrics: Statistical Methods Based on Ranks , 1976 .

[23]  D. Rubin Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[24]  Kjell A. Doksum,et al.  Empirical Probability Plots and Statistical Inference for Nonlinear Models in the Two-Sample Case , 1974 .

[25]  G. Imbens,et al.  What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments , 2008 .

[26]  Xiaohong Chen Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models , 2007 .

[27]  D. Rubin,et al.  Matched Sampling for Causal Effects: The Central Role of the Propensity Score in Observational Studies for Causal Effects , 2006 .

[28]  G. Imbens,et al.  Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components : A Re-Analysis of the California GAIN Program * by , 2005 .

[29]  V. Bentkus A Lyapunov-type Bound in Rd , 2005 .

[30]  Sergio Firpo Efficient Semiparametric Estimation of Quantile Treatment Effects , 2004 .

[31]  G. Imbens,et al.  Large Sample Properties of Matching Estimators for Average Treatment Effects , 2004 .

[32]  V. Bentkus,et al.  A Lyapunov type bound in $R^d$@@@A Lyapunov-type bound in $R^d$ , 2004 .

[33]  Jeffrey M. Woodbridge Econometric Analysis of Cross Section and Panel Data , 2002 .

[34]  V. Chernozhukov,et al.  An IV Model of Quantile Treatment Effects , 2002 .

[35]  Richard Blundell,et al.  ALTERNATIVE APPROACHES TO EVALUATION IN EMPIRICAL MICROECONOMICS , 2002 .

[36]  R. Rohh ALTERNATIVE METHODS FOR EVALUATING THE IMPACT OF INTERVENTIONS An Overview , 2001 .

[37]  J. Heckman,et al.  The Economics and Econometrics of Active Labor Market Programs , 1999 .

[38]  Toshio Honda Testing the goodness of fit of a linear model by Kernel regression , 1998 .

[39]  P. Robinson,et al.  Pooling nonparametric estimates of regression functions with similar shape , 1995 .

[40]  James Stephen Marron,et al.  Semiparametric Comparison of Regression Curves , 1990 .