Correction to: Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes (Journal of the American Statistical Association, (2018), 113, 522, (933-947), 10.1080/01621459.2018.1434530);Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes

Journal of the American Statistical Association

Volumn 113, Issue 522, 2018, Pages 933-947

  Swanson, Sonja A ^a,b   Hernán, Miguel A ^b,c   Miller, Matthew ^b,d   Robins, James M ^b   Richardson, Thomas S ^e  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

^d NORTHEASTERN UNIVERSITY   (United States)

^e UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Average treatment effect; Causal graphical model; Instrument; Instrumental variable; Partial identification; Single world intervention graph

Indexed keywords

EID: 85050572558     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2020.1724472     Document Type: Erratum

References (61)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B., (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–455.
2. Baiocchi, M., Cheng, J., and Small, D. S., (2014), “Instrumental Variable Methods for Causal Inference,” Statistics in Medicine, 33, 2297–2340.
3. Baker, S. G., and Lindeman, K. S., (1994), “The Paired Availability Design: A Proposal for Evaluating Epidural Analgesia During Labor,” Statistics in Medicine, 13, 2269–2278.
4. Balke, A., and Pearl, J., (1997), “Bounds on Treatment Effects From Studies With Imperfect Compliance,” Journal of the American Statistical Association, 92, 1171–1176.
5. Beresteanu, A., and Manski, C. F., (2000), “Bounds for Stata,” available at http://faculty.wcas.northwestern.edu/cfm754/bounds_stata.pdf.
6. Beresteanu, A., Molchanov, I., and Molinari, F., (2012), “Partial Identification Using Random Set Theory,” Journal of Econometrics, 166, 17–32.
7. Bhattacharya, J., Shaikh, A. M., and Vytlacil, E., (2008), “Treatment Effect Bounds Under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization,” The American Economic Review, 98, 351–356.
8. Blanco, G., Flores, C. A., and Flores-Lagunes, A., (2013), “Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages,” Journal of Human Resources, 48, 659–701.
9. Bonet, B., (2001), “Instrumentality Tests Revisited,” in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., pp. 48–55.
10. Cai, Z., Kuroki, M., Pearl, J., and Tian, J., (2008), “Bounds on Direct Effects in the Presence of Confounded Intermediate Variables,” Biometrics, 64, 695–701.
11. Chernozhukov, V., and Hansen, C., (2005), “An IV Model of Quantile Treatment Effects,” Econometrica, 73, 245–261.
12. Chernozhukov, V., Kim, W., Lee, S., and Rosen, A. M., (2015), “Implementing Intersection Bounds in Stata,” Stata Journal, 15, 21–44.
13. Chesher, A., (2010), “Instrumental Variable Models for Discrete Outcomes,” Econometrica, 78, 575–601.
14. Davies, N. M., Smith, G. D., Windmeijer, F., and Martin, R. M., (2013), “Issues in the Reporting and Conduct of Instrumental Variable Studies: A Systematic Review,” Epidemiology, 24, 363–369.
15. Dawid, A., (2003), “Causal Inference Using Influence Diagrams: The Problem of Partial Compliance,” in Highly Structured Stochastic Systems, eds. P. J., Green, N. L., Hjort, and S., Richardson, Oxford: Oxford University Press, pp. 45–81.
16. Didelez, V., Meng, S., and Sheehan, N. A., (2010), “Assumptions of IV Methods for Observational Epidemiology,” Statistical Science, 25, 22–40.
17. Didelez, V., and Sheehan, N., (2007), “Mendelian Randomization as an Instrumental Variable Approach to Causal Inference,” Statistical Methods in Medical Research, 16, 309–330.
18. Finkelstein, A., (2013), “Oregon Health Insurance Experiment Public Use Data,” available at http://www.nber.org/oregon/data.html
19. Flores, C. A., and Flores-Lagunes, A., (2013), “Partial Identification of Local Average Treatment Effects With an Invalid Instrument,” Journal of Business & Economic Statistics, 31, 534–545.
20. Greenland, S., Pearl, J., and Robins, J. M., (1999), “Causal Diagrams for Epidemiologic Research,” Epidemiology, 10, 37–48.
21. Heckman, J. J., and Vytlacil, E. J., (2001), Instrumental Variables, Selection Models, and Tight Bounds on the Average Treatment Effect, New York: Springer.
22. Hernán, M. A., and Robins, J. M., (2006), “Instruments for Causal Inference: An Epidemiologist’s Dream?” Epidemiology, 17, 360–372. [Errata in Epidemiology, 2014 Jan; 15(1): 164].
23. Huber, M., (2014), “Sensitivity Checks for the Local Average Treatment Effect,” Economics Letters, 123, 220–223.
24. Huber, M., Laffers, L., and Mellace, G., (2015), “Sharp IV Bounds on Average Treatment Effects on the Treated and Other Populations Under Endogeneity and Noncompliance,” Journal of Applied Econometrics, 32, 56–79.
25. Huber, M., and Mellace, G., (2015), “Testing Instrument Validity for Late Identification Based on Inequality Moment Constraints,” Review of Economics and Statistics, 97, 398–411.
26. Hudgens, M. G., Hoering, A., and Self, S. G., (2003), “On the Analysis of Viral Load Endpoints in HIV Vaccine Trials,” Statistics in Medicine, 22, 2281–2298.
27. Imai, K., (2008), “Sharp Bounds on the Causal Effects in Randomized Experiments With ‘Truncation-by-Death’,” Statistics & Probability Letters, 78, 144–149.
28. Imbens, G. W., and Angrist, J. D., (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475.
29. Kaufman, S., Kaufman, J. S., and MacLehose, R. F., (2009), “Analytic Bounds on Causal Risk Differences in Directed Acyclic Graphs Involving Three Observed Binary Variables,” Journal of Statistical Planning and Inference, 139, 3473–3487.
30. Kitagawa, T., (2009), “Identification Region of the Potential Outcome Distributions Under Instrument Independence,” Technical Report, working paper CWP30/09, London, UK: CEMMAP.
31. ——— (2015), “A Test for Instrument Validity,” Econometrica, 83, 2043–2063.
32. Kreider, B., and Hill, S. C., (2009), “Partially Identifying Treatment Effects With an Application to Covering the Uninsured,” Journal of Human Resources, 44, 409–449.
33. Manski, C. F., (1990), “Nonparametric Bounds on Treatment Effects,” The American Economic Review, 80, 319–323.
34. ——— (1997), “Monotone Treatment Response,” Econometrica: Journal of the Econometric Society, 65, 1311–1334.
35. ——— (2003), Partial Identification of Probability Distributions, New York: Springer Science & Business Media.
36. Manski, C. F., and Pepper, J. V., (2000), “Monotone Instrumental Variables: With an Application to the Returns to Schooling,” Econometrica, 68, 997–1010.
37. ——— (2015), “How do Right-to-Carry Laws Affect Crime Rates? Coping With Ambiguity Using Bounded-Variation Assumptions,” Review of Economics and Statistics, 100, 232–244.
38. McCarthy, I., Millimet, D. L., and Roy, M., (2015), “Bounding Treatment Effects: Stata Command for the Partial Identification of the Average Treatment Effect With Endogenous and Misreported Treatment Assignment,” Stata Journal, 15, 411–436.
39. Mealli, F., and Pacini, B., (2013), “Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance,” Journal of the American Statistical Association, 108, 1120–1131.
40. Mourifie, I., and Wan, Y., (2014), “Testing Local Average Treatment Effect Assumptions,” Review of Economics and Statistics, 99, 305–313.
41. Palmer, T. M., Ramsahai, R. R., Didelez, V., and Sheehan, N. A., (2011), “Nonparametric Bounds for the Causal Effect in a Binary Instrumental-Variable Model,” Stata Journal, 11, 345–367.
42. Pearl, J., (1995), “On the Testability of Causal Models With Latent and Instrumental Variables,” in Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI), Burlington: Morgan Kaufmann, pp. 435–443.
43. ——— (2000), Causality: Models, Reasoning and Inference(Vol. 29), Cambridge: Cambridge University Press. [Errata Available at: http://bayes.cs.ucla.edu/BOOK-09/causality2-errata-updated7_3_13.pdf].
44. Ramsahai, R. R., (2012), “Causal Bounds and Observable Constraints for Non-Deterministic Models,” The Journal of Machine Learning Research, 13, 829–848.
45. Richardson, T. S., Evans, R. J., and Robins, J. M., (2010), “Transparent Parametrizations of Models for Potential Outcomes,” in Bayesian Statistics 9, eds. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, Oxford: Oxford University Press, pp. 569–610.
46. Richardson, T. S., and Robins, J. M., (2010), “Analysis of the Binary Instrumental Variable Model,” in Heuristics, Probability and Causality: A Tribute to Judea Pearl, eds. R. Dechter, H. Geffner, and J. Y. Halpern, London: London College Publications, pp. 415–444.
47. ——— (2013), “Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality,” Working Paper 128, Center for the Statistics and the Social Sciences, University of Washington Series.
48. ——— (2014), “ACE Bounds; SEMs With Equilibrium Conditions,” Statistical Science, 29, 363–366.
49. Robins, J., (1986), “A New Approach to Causal Inference in Mortality Studies With Sustained Exposure Periods–Applications to Control of the Healthy Worker Survivor Effect,” Mathematical Modeling, 7, 1393–1512.
50. ——— (1989), The Analysis of Randomized and Non-Randomized AIDS Treatment Trials Using a New Approach to Causal Inference in Longitudinal Studies, Washington, DC: NCHRS, US Public Health Service.
51. ——— (1994), “Correcting for Non-Compliance in Randomized Trials Using Structural Nested Mean Models,” Communications in Statistics-Theory and Methods, 23, 2379–2412.
52. Robins, J. M., and Greenland, S., (1996), “Identification of Causal Effects Using Instrumental Variables: Comment,” Journal of the American Statistical Association, 91, 456–458.
53. Robins, J. M., and Richardson, T. S., (2011), “Alternative Graphical Causal Models and the Identification of Direct Effects,” in Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures, eds. P., Shrout, K., Keyes, and K., Ornstein, Oxford: Oxford University Press, pp. 1–52.
54. Siddique, Z., (2013), “Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence,” Journal of the American Statistical Association, 108, 504–513.
55. Spirtes, P., Glymour, C., and Scheines, R., (1993), Causation, Prediction and Search(Lecture Notes in Statistics, Vol. 81), New York: Springer-Verlag.
56. Sprecher, D. L., Schaefer, E. J., Kent, K. M., Gregg, R. E., Zech, L. A., Hoeg, J. M., McManus, B., Roberts, W. C., and Brewer, H. B., (1984), “Cardiovascular Features of Homozygous Familial Hypercholesterolemia: Analysis of 16 Patients,” The American Journal of Cardiology, 54, 20–30.
57. Swanson, S. A., Holme, Ø., Løberg, M., Kalager, M., Bretthauer, M., Hoff, G., Aas, E., and Hernán, M. A., (2015), “Bounding the Per-Protocol Effect in Randomized Trials: An Application to Colorectal Cancer Screening,” Trials, 16, 1–11.
58. Taubman, S. L., Allen, H. L., Wright, B. J., Baicker, K., and Finkelstein, A. N., (2014), “Medicaid Increases Emergency-Department Use: Evidence From Oregon’s Health Insurance Experiment,” Science, 343, 263–268.
59. VanderWeele, T. J., Tchetgen, E. J. T., Cornelis, M., and Kraft, P., (2014), “Methodological Challenges in Mendelian Randomization,” Epidemiology, 25, 427–435.
60. Wang, L., and Tchetgen, E. T., (2018), “Bounded, Efficient and Multiply Robust Estimation of Average Treatment Effects Using Instrumental Variables,” arXiv preprint arXiv:1611.09925.
61. Zhang, J. L., and Rubin, D. B., (2003), “Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “death”,” Journal of Educational and Behavioral Statistics, 28, 353–368.