[1] Arild Aakvik, James J. Heckman,, and Edward J. Vytlacil E.J. Estimating treatment effects for discrete outcomes when responses to treatment: vary an application to Norwegian vocational rehabilitation programs. Journal of Econometrics, 124(1-2):15–51, 2005.
[2] Jaap H. Abbring and James J. Heckman. Chapter 72 Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation. Handbook of Econometrics, 6:5145 – 5303, 2007.
[3] Aanders M., Björklund and Robert Moffitt. The Estimation of Wage Gains and Welfare Gains in Self-Selection Models. The Review of Economics and Statistics, 69(1):42–49, 1987.
[4] Martin Browning, Lars Peter Hansen, and James J. Heckman. Chapter 8 Micro Data and General Equilibrium Models. Handbook of Macroeconomics, 1:543 – 633, 1999.
[5] William G. Cochran and Donald B. Rubin. Controlling Bias in Observational Studies: A Review. Sankhya: The Indian Journal of Statistics Series A, 35(4):417–446, 1972.
[6] Philipp Eisenhauer, James J. Heckman, and Edward Vytlacil. The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs. Journal of Political Economy, 123(2):413–443, 2015.
[7] James J. Heckman. Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture. Journal of Political Economy, 109(4):673–748, 2001.
[8] James J. Heckman. Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy. Jounral of Economic Literature, 48(2):356–398, 2010.
[9] James J. Heckman, Hidehiko Ichimura, Jeffrey Smith, and Petra Todd. Characterizing Selection Bias Using Experimental Data. Journal of Political Economy, 66(5):1017–1098, 1998.
[10] James J. Heckman and Edward J. Vyrlacil. Policy-Relevant Treatment Effects. American Economic Review, 91(2):107–111, 2001.
[11] James J. Heckman and Edward J. Vyrlacil. Structural Equations, Treatment Effects, and Econometric Policy Evaluation. Econometrica, 73(3):669–739, 2005.
[12] James J. Heckman and Edward J. Vytlacil. Chapter 70 Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation. Handbook of Econometrics, 6:4779 – 4874, 2007.
[13] James J. Heckman and Edward J. Vytlacil. Chapter 71 Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments. Handbook of Econometrics, 6:4875 – 5143, 2007.
[14] James J.. Heckman, Jeffrey Smith, and Nancy Clements. Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts. The Review of Economic Studies, 64(4):487–535, 1997.
[15] Sergio Urzua , Heckman James J. and Edward J. Vytlacil. Understanding Instrumental Variables in Models with Essential Heterogeneity. Journal of Political Economy, 88(3):389–432, 2006.
[16] Paul W. Holland. Statistics and Causal Inference. Journal of the American Statistical Association, 81(386):945–960, 1986.
[17] Ralph I. Horwitz, Burton H. Singer, Robert. W. Makuch, and Catherine M. Viscoli. Can Treatment that is Helpful on Average be Harmful to Some Patients? A Study of Conflicting Information Needs of Clinical Inquiry and Drug Regulation. Journal of Clinical Epidemiology, 49(4):395–400, 1996.
[18] Ralph I. Horwitz, Burton H. Singer, Robert. W. Makuch, and Catherine M. Viscoli. Reaching the Tunnel at the End of the Light. Journal of Clinical Epidemiology, 50(7):753–755, 1997.
[19] Guido. W. Imbens and Joshua D. Angrist. Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2):467–475, 1994.
[20] Richard E. Quandt. The Estimation of the Parameters of a Linear Regression System Obeying two Seperate Regimes. Journal of the American Statistical Association, 53(284):873–880, 1958.
[21] Richard E. Quandt. A New Approach to Estimating Switching Regressions. Journal of the American Statistical Association, 67(338):306–310, 1972.
[22] Paul R. Rosenbaum and Donald B. Rubin. The central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1):41–55, 1983.
[23] A. D. Roy. Some Thoughts on the Distribution of Earnings. Oxford Economic Papers, 3(2):135–146, 1951.