Abadie 新作: 简明 IV, DID, RDD 教程和综述

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Abadie, A., M. D. Cattaneo, 2018, Econometric methods for program evaluation, MIT working paper. PDF

Abadie and Cattaneo (2018)-RDD, IV, DID, PSM

提要： Our goal in this article is to provide a summary overview of the literature on the econometrics of program evaluation for ex-post analysis and, in the process, to delineate some of the directions along which it is expanding, discussing recent developments and the areas where research may be particularly fruitful in the near future.
Other recent surveys on the estimation of causal treatment effects and the econometrics of program evaluation from different perspectives and disciplines include Abadie (2005a), Angrist and Pischke (2008, 2014), Athey and Imbens (2017c), Blundell and Costa Dias (2009), DiNardo and Lee (2011), Heckman and Vytlacil (2007), Hern´an and Robins (2018), Imbens and Rubin (2015), Imbens and Wooldridge (2009), Lee (2016), Manski (2008), Pearl (2009), Rosenbaum (2002, 2010), Van der Laan and Robins (2003), and VanderWeele (2015), among many others.

未来的研究方向

• 大数据
• 机器学习
• networks, spillovers, social interactions

An important recent development that has had a profound impact in the program evaluation literature is the arrival of new data environments (Mullainathan and Spiess, 2017). The availability of big data has generated the need for methods able to cope with data sets that are either too large or too complex to be analyzed using standard econometric methods. Of particular importance is the role of administrative records and of large data sets collected by automated systems.

These are, in some cases, relatively new source of information and pose challenges in terms of identification, estimation and inference. Model selection, shrinkage and empirical Bayes approaches are particular useful in this context (e.g., Efron, 2012; Abadie and Kasy, 2017), though these methods have not yet been fully incorporated into the program evaluation toolkit. Much of the current research in this area develops machine learning methods to estimate heterogeneous treatment effects in contexts with many covariates (see, e.g., Athey and Imbens, 2016; Wager and Athey, 2017; Taddy et al., 2016).

Also potentiated by the rise of new big, complex data, is the very recent work on networks, spillovers, social interactions, and interference (Graham, 2015; Graham and de Paula, 2018). While certainly of great importance for policy, these research areas are still evolving and not yet fully incorporated into the program evaluation literature. Bringing developments in these relatively new areas to the analysis and interpretation of policy and treatment effects is important to improve policy prescriptions.

Finally, because of space limitations, there are important topics not covered in this review. Among them are mediation analysis (VanderWeele, 2015; Lok, 2016), dynamic treatment effects and duration models (Abbring and Van den Berg, 2003; Abbring and Heckman, 2007), bounds and partial identification methods (Manski, 2008; Tamer, 2010), and optimal design of policies (Hirano and Porter, 2009; Kitagawa and Tetenov, 2018). These are also important areas of active research in the econometrics of program evaluation.

政策评价和因果推断方面的书籍和综述 （待更新）

• Abadie, A. (2005a). Causal inference. In Kempf-Leonard, K., editor, Encyclopedia of Social Measurement, vol. 1, pages 259–266. Academic.

• Abbring, J. H. and Heckman, J. J. (2007). Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation. In Heckman, J. and Leamer, E., editors, Handbook
  of Econometrics, vol. VI, pages 5145–5303. Elsevier Science B.V

• Angrist, J. D. and Pischke, J. S. (2008). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.

• Angrist, J. D. and Pischke, J. S. (2014). Mastering ’Metrics: The Path from Cause to Effect. Princeton University Press.
  Athey, S. and Imbens, G. W. (2017c). The state of applied econometrics: Causality and
  policy evaluation. Journal of Economic Perspectives, 31(2):3–32.

• Belloni, A., Chernozhukov, V., Fern´andez-Val, I., and Hansen, C. (2017). Program evaluation with high-dimensional data. Econometrica, 85(1):233–298.

• Bertrand, M., Duflo, E., and Mullainathan, S. (2004). How much should we trust differencesin-differences estimates?*. Quarterly Journal of Economics, 119(1):249–275

• Cattaneo, M. D. and Escanciano, J. C. (2017). Regression Discontinuity Designs: Theory and Applications (Advances in Econometrics, volume 38). Emerald Group Publishing.

• Cattaneo, M. D., Idrobo, N., and Titiunik, R. (2018a). A Practical -
  Introduction to Regression Discontinuity Designs: Part I. Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press, forthcoming

• Cattaneo, M. D., Idrobo, N., and Titiunik, R. (2018b). A Practical Introduction to Regression Discontinuity Designs: Part II. Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press, in preparation.

• DiNardo, J. and Lee, D. S. (2011). Program evaluation and research designs. In Ashenfelter, A. and Card, D., editors, Handbook of Labor Economics, vol. 4A, chapter 5, pages 463–536. Elsevier Science B.V.

• Ding, P. (2017). A paradox from randomization-based causal inference. Statistical Science, 32(3):331–345.

• Doudchenko, N. and Imbens, G. W. (2016). Balancing, regression, difference-in-differences and synthetic control methods: A synthesis. Working Paper 22791, National Bureau of Economic Research.

• Heckman, J. J. and Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. In Heckman, J. and Leamer, E., editors, Handbook of Econometrics, vol. VI, pages 4780–4874. Elsevier
  Science B.V.

• Hern´an, M. A. and Robins, J. M. (2018). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.

• Van der Laan, M. J. and Robins, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. Springer Science & Business Media.

• VanderWeele, T. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York.

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