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Articles

Double Machine Learning for Sample Selection Models

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References

  • Abowd, J., Crepon, B., and Kramarz, F. (2001), “Moment Estimation With Attrition: An Application to Economic Models,” Journal of the American Statistical Association, 96, 1223–1230. DOI: 10.1198/016214501753381878.
  • Ahn, H., and Powell, J. (1993), “Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism,” Journal of Econometrics, 58, 3–29. DOI: 10.1016/0304-4076(93)90111-H.
  • Angrist, J. D., and Pischke, J.-S. (2009), Mostly Harmless Econometrics: An Epiricist’s Companion, Princeton: Princeton University Press.
  • Athey, S., Chetty, R., Imbens, G. W., and Kang, H. (2019), “The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely,” Discussion paper, National Bureau of Economic Research.
  • Bang, H., and Robins, J. (2005), “Doubly Robust Estimation in Missing Data and Causal Inference Models,” Biometrics, 61, 962–972. DOI: 10.1111/j.1541-0420.2005.00377.x.
  • Barnwell, J.-L., and Chaudhuri, S. (2020), “Efficient Estimation in Sub and Full Populations with Monotonically Missing at Random Data,” working paper, McGill University, Montreal.
  • Belloni, A., and Chernozhukov, V. (2011), High Dimensional Sparse Econometric Models: An Introduction, Berlin: Springer.
  • Belloni, A., Chernozhukov, V., and Hansen, C. (2014), “Inference on Treatment Effects after Selection among High-Dimensional Controls,” The Review of Economic Studies, 81, 608–650. DOI: 10.1093/restud/rdt044.
  • Biewen, M., Fitzenberger, B., Osikominu, A., and Paul, M. (2014), “The Effectiveness of Public-Sponsored Training Revisited: The Importance of Data and Methodological Choices,” Journal of Labor Economics, 32, 837–897. DOI: 10.1086/677233.
  • Blundell, R. W., and Powell, J. L. (2004), “Endogeneity in Semiparametric Binary Response Models,” The Review of Economic Studies, 71, 655–679. DOI: 10.1111/j.1467-937X.2004.00299.x.
  • Bodory, H., and Huber, M. (2018), “The Causalweight Package for Causal Inference in R,” SES working paper 493, University of Fribourg.
  • Bodory, H., Huber, M., and Lafférs, L. (2022), “Evaluating (Weighted) Dynamic Treatment Effects by Double Machine Learning,” The Econometrics Journal, 25, 628–648. DOI: 10.1093/ectj/utac018.
  • Carroll, R., Ruppert, D., and Stefanski, L. (1995), Measurement Error in Nonlinear Models, London: Chapman and Hall.
  • Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. (2018), “Double/Debiased Machine Learning for Treatment and Structural Parameters,” The Econometrics Journal, 21, C1–C68. DOI: 10.1111/ectj.12097.
  • Das, M., Newey, W. K., and Vella, F. (2003), “Nonparametric Estimation of Sample Selection Models,” Review of Economic Studies, 70, 33–58. DOI: 10.1111/1467-937X.00236.
  • d’Haultfoeuille, X. (2010), “A New Instrumental Method for Dealing with Endogenous Selection,” Journal of Econometrics, 154, 1–15. DOI: 10.1016/j.jeconom.2009.06.005.
  • Farrell, M. H., Liang, T., and Misra, S. (2021), “Deep Neural Networks for Estimation and Inference,” Econometrica, 89, 181–213. DOI: 10.3982/ECTA16901.
  • Fitzgerald, J., Gottschalk, P., and Moffitt, R. (1998), “An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics,” Journal of Human Resources, 33, 251–299. DOI: 10.2307/146433.
  • Flores, C. A., and Flores-Lagunes, A. (2009), “Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness,” IZA DP No. 4237.
  • Flores, C. A., and Flores-Lagunes, A. (2010), “Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects,” mimeo, University of Florida.
  • Flores, C. A., Flores-Lagunes, A., Gonzales, A., and Neuman, T. (2012), “Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps,” The Review of Economics and Statistics, 94, 153–171. DOI: 10.1162/REST_a_00177.
  • Frangakis, C., and Rubin, D. (2002), “Principal Stratification in Causal Inference,” Biometrics, 58, 21–29. DOI: 10.1111/j.0006-341x.2002.00021.x.
  • Frölich, M., and Huber, M. (2017), “Direct and Indirect Treatment Effects: Causal Chains and Mediation Analysis with Instrumental Variables,” Journal of the Royal Statistical Society, Series B, 79, 1645–1666. DOI: 10.1111/rssb.12232.
  • Frumento, P., Mealli, F., Pacini, B., and Rubin, D. B. (2012), “Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data,” Journal of the American Statistical Association, 107, 450–466. DOI: 10.1080/01621459.2011.643719.
  • Hausman, J., and Wise, D. (1979), “Attrition Bias In Experimental and Panel Data: The Gary Income Maintenance Experiment,” Econometrica, 47, 455–473. DOI: 10.2307/1914193.
  • Heckman, J. (1976), “The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables, and a Simple Estimator for such Models,” Annals of Economic and Social Measurement, 5, 475–492.
  • Heckman, J., (1979), “Sample Selection Bias as a Specification Error,” Econometrica, 47, 153–161. DOI: 10.2307/1912352.
  • Huber, M. (2012), “Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition,” Journal of Educational and Behavioral Statistics, 37, 443–474. DOI: 10.3102/1076998611411917.
  • Huber, M. (2014a), “Identifying Causal Mechanisms (Primarily) based on Inverse Probability Weighting,” Journal of Applied Econometrics, 29, 920–943.
  • Huber, M. (2014b), “Treatment Evaluation in the Presence of Sample Selection,” Econometric Reviews, 33, 869–905.
  • Huber, M., and Melly, B. (2015), “A Test of the Conditional Independence Assumption in Sample Selection Models,” Journal of Applied Econometrics, 30, 1144–1168. DOI: 10.1002/jae.2431.
  • Imai, K., and Yamamoto, T. (2013), “Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments,” Political Analysis, 21, 141–171. DOI: 10.1093/pan/mps040.
  • Imbens, G. W. (2004): “Nonparametric estimation of average treatment effects under exogeneity: a review,” The Review of Economics and Statistics, 86, 4–29. DOI: 10.1162/003465304323023651.
  • Imbens, G. W., and Newey, W. K. (2009), “Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity,” Econometrica, 77, 1481–1512.
  • Imbens, G. W., and Wooldridge, J. M. (2009), “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, 47, 5–86. DOI: 10.1257/jel.47.1.5.
  • Kueck, J., Luo, Y., Spindler, M., and Wang, Z. (2023), “Estimation and Inference of Treatment Effects with L2-boosting in High-Dimensional Settings,” Journal of Econometrics, 234, 714–731. DOI: 10.1016/j.jeconom.2022.02.005.
  • Lechner, M. (2009), “Sequential Causal Models for the Evaluation of Labor Market Programs,” Journal of Business and Economic Statistics, 27, 71–83. DOI: 10.1198/jbes.2009.0006.
  • Lechner, M., and Wunsch, C. (2013), “Sensitivity of Matching-based Program Evaluations to the Availability of Control Variables,” Labour Economics, 21, 111–121. DOI: 10.1016/j.labeco.2013.01.004.
  • Lee, D. S. (2009), “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects,” Review of Economic Studies, 76, 1071–1102. DOI: 10.1111/j.1467-937X.2009.00536.x.
  • Levy, J. (2019), “Tutorial: Deriving The Efficient Influence Curve for Large Models,” arXiv preprint arXiv:1903.01706.
  • Little, R., and Rubin, D. (1987), Statistical Analysis with Missing Data, New York: Wiley.
  • Little, R. J. A. (1995), “Modeling the Drop-Out Mechanism in Repeated-Measures Studies,” Journal of the American Statistical Association, 90, 1112–1121. DOI: 10.1080/01621459.1995.10476615.
  • Negi, A. (2020), “Doubly Weighted M-estimation for Nonrandom Assignment and Missing Outcomes,” arXiv preprint arXiv:2011.11485.
  • Newey, W., Powell, J., and Vella, F. (1999), “Nonparametric Estimation of Triangular Simultaneous Equations Models,” Econometrica, 67, 565–603. DOI: 10.1111/1468-0262.00037.
  • Newey, W. K. (2007), “Nonparametric Continuous/Discrete Choice Models,” International Economic Review, 48, 1429–1439. DOI: 10.1111/j.1468-2354.2007.00469.x.
  • Neyman, J. (1959), “Optimal Asymptotic Tests of Composite Statistical Hypotheses,” in Probability and Statistics, ed. V. Grenander, pp. 416–444, New York: Wiley.
  • Pearl, J. (2000), Causality: Models, Reasoning, and Inference, Cambridge: Cambridge University Press.
  • Robins, J. (1986), “A New Approach to Causal Inference in Mortality Studies with Sustained Exposure Periods - Application to Control of the Healthy Worker Survivor Effect,” Mathematical Modelling, 7, 1393–1512. DOI: 10.1016/0270-0255(86)90088-6.
  • Robins, J., Rotnitzky, A., and Zhao, L. (1995), “Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journal of American Statistical Association, 90, 106–121. DOI: 10.1080/01621459.1995.10476493.
  • Robins, J. M. (1998), “Marginal Structural Models,” in 1997 Proceedings of the American Statistical Association, Section on Bayesian Statistical Science, pp. 1–10.
  • Robins, J. M., Rotnitzky, A., and Zhao, L. (1994), “Estimation of Regression Coefficients When Some Regressors Are not Always Observed,” Journal of the American Statistical Association, 90, 846–866. DOI: 10.1080/01621459.1994.10476818.
  • Rubin, D. (1980), “Comment on ’Randomization Analysis of Experimental Data: The Fisher Randomization Test’ by D. Basu,” Journal of American Statistical Association, 75, 591–593. DOI: 10.2307/2287653.
  • Rubin, D. B. (1974), “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies,” Journal of Educational Psychology, 66, 688–701. DOI: 10.1037/h0037350.
  • Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581–592.
  • Schochet, P., Burghardt, J., and Glazerman, S. (2001), National Job Corps Study: The Impacts of Job Corps on Participants Employment and Related Outcomes, Report, Washington, DC: Mathematica Policy Research, Inc.
  • Schochet, P., Burghardt, J., and McConnell, S. (2008), “Does Job Corps Work? Impact Findings from the National Job Corps Study,” The American Economic Review, 98, 1864–1886. DOI: 10.1257/aer.98.5.1864.
  • Semenova, V. (2020), “Better Lee Bounds,” arXiv preprint arXiv:2008.12720.
  • Shah, A., Laird, N., and Schoenfeld, D. (1997), “A Random-Effects Model for Multiple Characteristics With Possibly Missing Data,” Journal of the American Statistical Association, 92, 775–779. DOI: 10.1080/01621459.1997.10474030.
  • Sloczyński, T., and Wooldridge, J. M. (2018), “A General Double Robustness Result for Estimating Average Treatment Effects,” Econometric Theory, 34, 112–133. DOI: 10.1017/S0266466617000056.
  • Tran, L., Yiannoutsos, C., Wools-Kaloustian, K., Siika, A., van der Laan, M., and Petersen, M. (2019), “Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study,” The International Journal of Biostatistics, 15, 1–27. DOI: 10.1515/ijb-2017-0054.
  • Wager, S., and Athey, S. (2018), “Estimation and Inference of Heterogeneous Treatment Effects using Random Forests,” Journal of the American Statistical Association, 113, 1228–1242. DOI: 10.1080/01621459.2017.1319839.
  • Wooldridge, J. (2002), “Inverse Probability Weigthed M-Estimators for Sample Selection, Attrition and Stratification,” Portuguese Economic Journal, 1, 141–162. DOI: 10.1007/s10258-002-0008-x.
  • Wooldridge, J. (2007), “Inverse Probability Weighted Estimation for General Missing Data Problems,” Journal of Econometrics, 141, 1281–1301.
  • Zhang, J., Rubin, D., and Mealli, F. (2009), “Likelihood-based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification,” Journal of the American Statistical Association, 104, 166–176. DOI: 10.1198/jasa.2009.0012.
  • Zhang, J., and Rubin, D. B. (2003), “Estimation of Causal Effects via Principal Stratification When Some Outcome are Truncated by Death,” Journal of Educational and Behavioral Statistics, 28, 353–368. DOI: 10.3102/10769986028004353.
  • Zhang, J., Rubin, D. B., and Mealli, F. (2008), “Evaluating the Effects of Job Training Programs on Wages through Principal Stratification,” in Advances in Econometrics: Modelling and Evaluating Treatment Effects in Econometrics (Vol. 21), eds. D. Millimet, J. Smith, and E. Vytlacil, pp. 117–145, Bingley: Emerald Group Publishing Limited.

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