Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs

The Oxford Handbook of Quantitative Methods: Volume 1: Foundations

Volumn 1, Issue , 2013, Pages 223-236

  Hallberg, Kelly ^a   Wing, Coady ^a   Wong, Vivian ^a   Cook, Thomas D ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Causal inference; quasi experiments; randomized controlled trials; regression discontinuity designs

Indexed keywords

EID: 85109751229     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/oxfordhb/9780199934874.013.0012     Document Type: Chapter

References (38)

1. Aiken, L. S., West, S. G., Schwalm, D. E., Carroll, J., and Hsuing, S. (1998). Comparison of a randomized and two quasi-experimental designs in a single outcome evaluation: Efficacy of a university-level remedial writing program. Evaluation Review, 22, 207—244.
2. Black, D., Galdo, J., and Smith, J.A. (2007). Evaluating the regression discontinuity design using experimental data. Retrieved from economics.uwo.ca/newsletter/misc/2009/smith_mar25.pdf. Accessed October 15, 2011.
3. Battistin, E. and Rettore, E. (2008). Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs. Journal of Econometrics, 142, 715-730.
4. Berk, R., Barnes, G., Ahlman, L., and Kurtz (2010).When second best is good enough: A comparison between a true experiment and a regression discontinuity quasi-experiment. Journal of Experimental Criminology.
5. Buddelmeyer, H., and Skoufias, E. (2004). An evaluation of the performance of regression discontinuity design on PROGRESA. World Bank Policy Research Working Paper No. 3386; IZA Discussion Paper No. 827. Retrieved from http://ssrn.com/abstract=434600. Accessed October 17, 2011.
6. Cook, T., Shadish, W., and Wong, V. (2008). Three conditions under which observational studies produce the same results as experiments. Journal of Policy Analysis and Management, 274, 724-750.
7. Cook, T.D. (2008). Waiting for lide to arrive: A history of regression discontinuity in psychology, statistics, and economics. Journal of Econometrics, 142(2), 636-654.
8. Cook, T. D., and Campbell, D. (1979). Quasi-experimental design and and analysis issues for field settings. Boston, MA: Houghton Mifflin.
9. Cook, T. D., and Wong, V. C. (2008). Better quasi-experimental practice. In P. Alasuutari, J. Brannen, and L. Bickman (Eds.), The Handbook of Social Research (pp. 134-165). London: Sage.
10. Cook, T. D., and Wong, V. C. (2008). Empirical tests of the validity of the regression-discontinuity design. Annales d’Economie et de Statistique.
11. Cox, J.R. (1958). Some problems connected with statistical inferences. The Annals of Mathematical Statistics, 29(2), 357-372.
12. Dinardo, J., and Lee, D. (2010). Program evaluation and research designs. Cambridge, MA: The National Bureau of Economic Research.
13. Garfinkel, I., Manski, C., and Michalopoulos, C. (1992). Micro experiments and macro effects. In C. Manski and I. Garfinkel (Eds.), In Evaluating Welfare and Training Programs. Cambridge, MA: Havard University Press.
14. Glazerman, S., Levy, D., and Myers, D. (2003). Non experimental versus experimental estimates of earnings impacts. The Annals of the American Academy, 589, 63-91.
15. Hahn, J. Todd, P., and Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression discontinuity design. Econometrica, 69(1), 201-209.
16. Heckman, J., Ichimura, H., and Todd, P. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training program. Review of Economic Studies, 64(3), 605-654.
17. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81 (396), 945-960.
18. Imbens, G., Angrist, J., and Rubin, D.B. (1996). Identification of causal effects using instrumental variables. Journal of Econometrics, 71(1), 145-160.
19. Krueger, A. and Whitmore, D. (2001). The effect of attending a small class in the early grades on college-test taking and middle school test results: Evidence from Project STAR. Economic Journal, 111, 1—28.
20. Lee, D., and Lemieux, T. (2009). Regression Discontinuity Design in Economics. Cambridge, MA: National Bureau of Economics Research.
21. Lemieux, T. and Milligan, K. (2008). Incentive effects of social assistance: A regression discontinuity approach. Journal of Econometrics, 142, 715—730.
22. McCrary, J. (2008). Manipulation of the running variable in regression discontinuity design: A density test. Journal of Econometrics, 142, 698—714.
23. Mishel, L. and Rothstein, R, (Eds.) (2002). The class size debate. Washington, DC: The Economic Policy Institute.
24. Neyman, J. (1923). Statistical problems in agricultural experiments. Journal of the Royal Statistical Association, 107-108.
25. Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66(4), 688-701.
26. Rubin, D.B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 4-58.
27. Rubin, D.B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34-58.
28. Rubin, D.B. (1990) Formal means of statistical inference for causal effects. Journal of Statistical Planning and Inference, 25, 279-292.
29. Schochet, P., Cook, T., Deke, J., Imbens, G., Lockwood, J., Porter, J., et al. (2010). Standards for Regression Discontinuity. Retrieved October 15, 2010, from What Works Clearinghouse: http://ies.ed.gov/ncee/wwc/pdf/wwc_rd.pdf.
30. Schochet, P.Z. (2009). Statistical power for regression discontinuity designs in education evaluations. Journal of Educational and Behavioral Statistics, 34(2), 238-266.
31. Shadish, W.R., Clark, W.R., and Steiner, P.M. (2008). Can randomized experiments yield accurate answers? A randomized experiment comparing random and non-random assignments. Journal of the American Statistical Association, 103(484), 1334-1336.
32. Shadish, W.R., Cook, T.D., and Campbell, D. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin Company.
33. Shadish, W. S., Galindo, R., Wong, V. C., Steiner, P. M., and Cook, T. D. (2011). A Randomized Experiment Comparing Random to Cutoff-Based Assignment. Psychological Methods.
34. Stetcher, B.M. and Bohrstedt, G.W., Eds. (2000). Class size reduction in California: The 1998-1999 evaluation findings. Sacremento, CA: California Department of Education.
35. Sween, J. A. (1971). The experimental regression design: An inquiry into the feasibility of nonrandom treatment allocation. Unpublished doctoral dissertation, Northwestern University, Evanston, IL.
36. Thistlewaite, D., and Campbell, D. (1960). Regression discontinuity analysis: An alternative to the ex-post-factor experiment. Journal of Educational Psychology, 51, 309-317.
37. Wong, V.C. (2010). Addressing theoretical and practical challenges in the Regression Discontinuity Design. Unpublished doctoral dissertation, Northwestern University, Evanston, IL.
38. Wong, V. C., Cook, T. D., Barnett, W. S., and Jung, K. (2008). An effectiveness-based evaluation of five state pre-kindergarten programs. Journal of Policy Analysis and Management, 27(1), 122-154.