Imbens G, Mealli F, Ferro M, Biggeri M. Analyzing a Randomized Trial on Breast Self-examination with Noncompliance and Missing Outcomes. Biostatistics. 2004;5 (2) :2007-2222. PDF
Imbens G, Abadie A, Drukker D, Herr J. Implementing Matching Estimators for Average Treatment Effects in Stata. The STATA Journal. 2004;4 (3) :290-311.Abstract

This paper presents an implementation of matching estimators for average treatment effects in Stata. The nnmatch command allows you to estimate the average effect for all units or only for the treated or control units; to choose the number of matches; to specify the distance metric; to select a bias adjustment;and to use heteroskedastic-robust variance estimators.

Imbens G, Chamberlain G. Random Effects Estimators with Many Instrumental Variables. Econometrica. 2004;72 (1) :295-306. PDF
Imbens G, Manski C. Confidence Intervals for Partially Identified Parameters. Econometrica. 2004;72 (6) :1845-1857. PDF
Imbens G, Hirano K, Ridder G. Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica. 2003;71 (4) :1161-1189. PDF
Imbens G, Donald S, Newey W. Empirical Likelihood Estimation and Consistent Tests with Conditional Moment Restrictions. Journal of Econometrics. 2003;117 (1) :55-93. PDF
Imbens G. Sensitivity to Exogeneity Assumptions in Program Evaluation. The American Economic Review. 2003. PDF
Imbens G, Chamberlain G. Nonparametric Application of Bayesian Inference. Journal of Business and Economic Statistics. 2003. PDF
Imbens G, Angrist J. Comment on: 'Covariance Adjustment in Randomized Experiments and Observational Studies', by Paul Rosenbaum. Statistical Science. 2002;17 (3) :304-307. PDF
Imbens G. Generalized method of moments and empirical likelihood. Journal of Business and Economic Statistics. 2002;20 (4) :493-506. PDF
Imbens G, Abadie A, Angrist J. Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica. 2002;70 (1) :91-117. PDF
Imbens G, Spady R. Confidence Intervals in Generalized Method of Moments Models. Journal of Econometrics. 2002;107 :87-98. PDF
Imbens G. Some Remarks on Instrumental Variables. In: Lechner, Pfeiffer Econometric Evaluations of Labour Market Policies. Spring Verlag ; 2001.
Imbens G. Comment on: "Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice', by Joshua Angrist. Journal of Business and Economic Statistics. 2001;19 (1) :17-20. PDF
Imbens G, Rubin D, Sacerdote B. Estimating the Effect of Unearned Income on Labor Supply, Earnings, Savings and Consumption: Evidence from a Survey of Lottery Players. The American Economic Review. 2001;91 (4) :778-794.Abstract

Knowledge of the effect of unearned income on economic behavior of individuals in general, and on labor supply in particular, is of great importance to policy makers. Estimation of income effects, however, is a difficult problem because income is not randomly assigned and exogenous changes in income are difficult to identify. Here we exploit the randomized assignment of large amounts of money over long periods of time through lotteries. We carried out a survey of people who played the lottery in the mid-eighties and estimate the effect of lottery winnings on their subsequent earnings, labor supply, consumption, and savings. We find that winning a modest prize ($15,000 per year for twenty years) does not affect labor supply or earnings substantially. Winning such a prize does not considerably reduce savings. Winning a much larger prize ($80,000 rather than $15,000 per year) reduces labor supply as measured by hours, as well as participation and social security earnings; elasticities for hours and earnings are around -0.20 and for participation around -0.14. Winning a large versus modest amount also leads to increased expenditures on cars and larger home values, although mortgages values appear to increase by approximately the same amount. Winning $80,000 increases overall savings, although savings in retirement accounts are not significantly affected. The results do not vary much by gender, age, or prior employment status. There is some evidence that for those with zero earnings prior to winning the lottery there is a positive effect of winning a small prize on subsequent labor market participation.

Imbens G, Hyslop D. Bias from Classical and Other Forms of Measurement Error. Journal of Business and Economic Statistics. 2001;19 (October, 4) :475-481.Abstract

We consider the implications of a specific alternative to the classical measurement error model, in which the data are optimal predictions based on some information set. One motivation for this model is that if respondents are aware of their ignorance they may interpret the question 'what is the value of this variable?' as what is your best estimate of this variable?', and provide optimal predictions of the variable of interest given their information set. In contrast to the classical measurement error model, this model implies that the measurement error is uncorrelated with the reported value and, by necessity, correlated with the true value of the variable. In the context of the linear regression framework, we show that measurement error can lead to over- as well as under-estimation of the coefficients of interest. Critical for determining the bias is the model for the individual reporting the mismeasured variables, the individual's information set, and the correlation structure of the errors. We also investigate the implications of instrumental variables methods in the presence of measurement error of the optimal prediction error form and show that such methods may in fact introduce bias. Finally, we present some calculations indicating that the range of estimates of the returns to education consistent with amounts of measurement error found in previous studies. This range can be quite wide, especially if one allows for correlation between the measurement errors.

Hirano K. Estimation of Causal Effects Using Propensity Score Weighting: An Application to Data on Right Hear Catherization. Health Services and Outcome Research Methodology. 2001. PDF
Imbens G, Hirano K, Ridder G, Rubin D. Combining Panel Data Sets with Attrition and Refreshment Samples. Econometrica. 2001;69 (6) :1645-1659. PDF
Imbens G, Hirano K, Rubin D, Zhou X-H. Assessing the Effect of an influenza vaccine in an Encouragement Design. Biostatistics. 2000;1 (1) :69-88. PDF
Imbens G. The Role of the Propensity Score in Estimating Dose-Response Functions. Biometrika. 2000;87 (3) :706-710.Abstract

Estimation of average treatment effects in observational, or non-experimental in pre-treatment variables. If the number of pre-treatment variables is large, and their distribution varies substantially with treatment status, standard adjustment methods such as covariance adjustment are often inadequate. Rosenbaum and Rubin (1983) propose an alternative method for adjusting for pre-treatment variables based on the propensity score conditional probability of receiving the treatment given pre-treatment variables. They demonstrate that adjusting solely for the propensity score removes all the bias associated with differences in pre-treatment variables between treatment and control groups. The Rosenbaum-Rubin proposals deal exclusively with the case where treatment takes on only two values. In this paper an extension of this methodology is proposed that allows for estimation of average causal effects with multi-valued treatments while maintaining the advantages of the propensity score approach.