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Volumn 5, Issue 2 B, 2011, Pages 1428-1455

Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams

Author keywords

Causal bayesian networks; Causal inference; Nighttime crash data; Observational studies; Potential outcomes; Transportation safety

Indexed keywords


EID: 84870301423     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/10-AOAS440     Document Type: Article
Times cited : (34)

References (54)
  • 2
    • 33644851650 scopus 로고    scopus 로고
    • Doubly robust estimation in missing data and causal inference models
    • Mathematical Reviews (MathSciNet): MR2216189 Digital Object Identifier: doi:10.1111/j.1541-0420.2005.00377.x
    • Bang, H. and Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61 962-973. Mathematical Reviews (MathSciNet): MR2216189 Digital Object Identifier: doi:10.1111/j.1541-0420.2005.00377.x
    • (2005) Biometrics , vol.61 , pp. 962-973
    • Bang, H.1    Robins, J.M.2
  • 3
    • 0009602165 scopus 로고    scopus 로고
    • Bureau of Transportation Statistics, U.S, Technical report
    • Bureau of Transportation Statistics, U.S. (2007). National transportation statistics. Technical report.
    • (2007) National Transportation Statistics
  • 4
    • 67651098902 scopus 로고    scopus 로고
    • The consistency statement in causal inference: A definition or an assumption?
    • Cole, S. R. and Frangakis, C. E. (2009). The consistency statement in causal inference: A definition or an assumption? Epidemiology 20 3-5.
    • (2009) Epidemiology , vol.20 , pp. 3-5
    • Cole, S.R.1    Frangakis, C.E.2
  • 6
    • 0033987330 scopus 로고    scopus 로고
    • Accident reduction factors and causal inference in traffic safety studies: A review
    • Davis, G. A. (2000). Accident reduction factors and causal inference in traffic safety studies: A review. Accident Analysis & Prevention 32 95-109.
    • (2000) Accident Analysis & Prevention , vol.32 , pp. 95-109
    • Davis, G.A.1
  • 7
    • 4344707208 scopus 로고    scopus 로고
    • Possible aggregation biases in road safety research and a mechanism approach to accident modeling
    • Davis, G. A. (2004). Possible aggregation biases in road safety research and a mechanism approach to accident modeling. Accident Analysis & Prevention 36 1119-1127.
    • (2004) Accident Analysis & Prevention , vol.36 , pp. 1119-1127
    • Davis, G.A.1
  • 8
    • 0442309558 scopus 로고    scopus 로고
    • Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
    • Dehejia, R. H. and Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. J. Amer. Statist. Assoc. 94 1053-1062.
    • (1999) J. Amer. Statist. Assoc , vol.94 , pp. 1053-1062
    • Dehejia, R.H.1    Wahba, S.2
  • 10
    • 0004152755 scopus 로고
    • Press, Cambridge, Mathematical Reviews (MathSciNet): MR1120269 Zentralblatt MATH: 0785.60003
    • Eells, E. (1991). Probabilistic Causality. Cambridge Univ. Press, Cambridge. Mathematical Reviews (MathSciNet): MR1120269 Zentralblatt MATH: 0785.60003
    • (1991) Probabilistic Causality. Cambridge Univ
    • Eells, E.1
  • 11
    • 84873497502 scopus 로고    scopus 로고
    • Randomization, models, and the estimation of causal effects. Unpublished manuscript
    • Fienberg, S. E. and Sfer, A. M. (2006). Randomization, models, and the estimation of causal effects. Unpublished manuscript.
    • (2006)
    • Fienberg, S.E.1    Sfer, A.M.2
  • 12
    • 84873498317 scopus 로고    scopus 로고
    • Banjo: Bayesian network inference with Java objects. Software package, available at
    • Hartemink, A. J. (2005). Banjo: Bayesian network inference with Java objects. Software package, available at http://www.cs.duke.edu/amink/software/banjo.
    • (2005)
    • Hartemink, A.J.1
  • 13
    • 51849110999 scopus 로고    scopus 로고
    • A tutorial on learning with Bayesian networks
    • D. Holmes and L. Jain, eds.), Springer, Berlin
    • Heckerman, D. (2008). A tutorial on learning with Bayesian networks. In Innovations in Bayesian Networks (D. Holmes and L. Jain, eds.) 33-82. Springer, Berlin.
    • (2008) Innovations In Bayesian Networks , pp. 33-82
    • Heckerman, D.1
  • 14
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D., Geiger, D. and Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20 197-243.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 15
    • 0000423207 scopus 로고    scopus 로고
    • Matching as an econometric evaluation estimator
    • Mathematical Reviews (MathSciNet): MR1623713 Digital Object Identifier: doi:10.1111/1467-937X.00044
    • Heckman, J. J., Ichimura, H. and Todd, P. (1998). Matching as an econometric evaluation estimator. Rev. Econom. Stud. 65 261-294. Mathematical Reviews (MathSciNet): MR1623713 Digital Object Identifier: doi:10.1111/1467-937X.00044
    • (1998) Rev. Econom. Stud , vol.65 , pp. 261-294
    • Heckman, J.J.1    Ichimura, H.2    Todd, P.3
  • 16
    • 33745842238 scopus 로고    scopus 로고
    • Estimating causal effects from epidemiological data
    • Hernan, M. A. and Robins, J. M. (2006). Estimating causal effects from epidemiological data. J. Epidemiol. Community Health 60 578-586.
    • (2006) J. Epidemiol. Community Health , vol.60 , pp. 578-586
    • Hernan, M.A.1    Robins, J.M.2
  • 17
    • 0035761721 scopus 로고    scopus 로고
    • Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization
    • Hirano, K. and Imbens, G. W. (2001). Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology 2 259-278.
    • (2001) Health Services and Outcomes Research Methodology , vol.2 , pp. 259-278
    • Hirano, K.1    Imbens, G.W.2
  • 18
    • 84987736345 scopus 로고    scopus 로고
    • The propensity score with continuous treatments
    • X.-L. Meng and A. Gelman, eds.), Wiley, Chichester, Mathematical Reviews (MathSciNet): MR2134803 Zentralblatt MATH: 05274806
    • Hirano, K. and Imbens, G. W. (2004). The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (X.-L. Meng and A. Gelman, eds.) 73-84. Wiley, Chichester. Mathematical Reviews (MathSciNet): MR2134803 Zentralblatt MATH: 05274806
    • (2004) Applied Bayesian Modeling and Causal Inference From Incomplete-Data Perspectives , pp. 73-84
    • Hirano, K.1    Imbens, G.W.2
  • 19
    • 0141495120 scopus 로고    scopus 로고
    • Efficient estimation of average treatment effects using the estimated propensity score
    • Mathematical Reviews (MathSciNet): MR1995826 Digital Object Identifier: doi:10.1111/1468-0262.00442
    • Hirano, K., Imbens, G. W. and Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71 1161-1189. Mathematical Reviews (MathSciNet): MR1995826 Digital Object Identifier: doi:10.1111/1468-0262.00442
    • (2003) Econometrica , vol.71 , pp. 1161-1189
    • Hirano, K.1    Imbens, G.W.2    Ridder, G.3
  • 21
    • 84920420698 scopus 로고
    • Statistics and causal inference
    • Mathematical Reviews (MathSciNet): MR867618 Zentralblatt MATH: 0607.62001 Digital Object Identifier: doi:10.1080/01621459.1986.10478354
    • Holland, P. W. (1986). Statistics and causal inference. J. Amer. Statist. Assoc. 81 945-960. Mathematical Reviews (MathSciNet): MR867618 Zentralblatt MATH: 0607.62001 Digital Object Identifier: doi:10.1080/01621459.1986.10478354
    • (1986) J. Amer. Statist. Assoc , vol.81 , pp. 945-960
    • Holland, P.W.1
  • 22
    • 27144474493 scopus 로고    scopus 로고
    • Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics
    • Hong, G. and Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis 27 205-224.
    • (2005) Educational Evaluation and Policy Analysis , vol.27 , pp. 205-224
    • Hong, G.1    Raudenbush, S.W.2
  • 24
    • 78651509637 scopus 로고    scopus 로고
    • Predicting pavement marking retroreflectivity degradation using artificial neural networks: An exploratory analysis
    • Karwa, V. and Donnell, E. T. (2011). Predicting pavement marking retroreflectivity degradation using artificial neural networks: An exploratory analysis. Journal of Transportation Engineering 137 91-103.
    • (2011) Journal of Transportation Engineering , vol.137 , pp. 91-103
    • Karwa, V.1    Donnell, E.T.2
  • 25
    • 84870301423 scopus 로고    scopus 로고
    • Supplement to "Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams." DOI: 10.1214/10-AOAS440SUPP, Mathematical Reviews (MathSciNet): MR2849781 Zentralblatt MATH: 1223.62175 Digital Object Identifier: doi:10.1214/10-AOAS440 Project Euclid: euclid.aoas/1310562728
    • Karwa, V., Slavković, A. and Donnell, E. T. (2011). Supplement to "Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams." DOI: 10.1214/10-AOAS440SUPP. Mathematical Reviews (MathSciNet): MR2849781 Zentralblatt MATH: 1223.62175 Digital Object Identifier: doi:10.1214/10-AOAS440 Project Euclid: euclid.aoas/1310562728
    • (2011)
    • Karwa, V.1    Slavković, A.2    Donnell, E.T.3
  • 26
    • 0001926461 scopus 로고    scopus 로고
    • Causal inference from graphical models
    • Chapman & Hall/CRC Press, Boca Raton, FL, Mathematical Reviews (MathSciNet): MR1893411 Zentralblatt MATH: 1010.62004
    • Lauritzen, S. L. (1999). Causal inference from graphical models. In Complex Stochastic Systems 63-107. Chapman & Hall/CRC Press, Boca Raton, FL. Mathematical Reviews (MathSciNet): MR1893411 Zentralblatt MATH: 1010.62004
    • (1999) Complex Stochastic Systems , pp. 63-107
    • Lauritzen, S.L.1
  • 27
    • 74749097452 scopus 로고    scopus 로고
    • Improving propensity score weighting using machine learning
    • Lee, B. K., Lessler, J. and Stuart, E. A. (2009). Improving propensity score weighting using machine learning. Stat. Med. 29 337-346. Mathematical Reviews (MathSciNet): MR2750549
    • (2009) Stat. Med , vol.29 , pp. 337-346
    • Lee, B.K.1    Lessler, J.2    Stuart, E.A.3
  • 28
    • 4344579428 scopus 로고    scopus 로고
    • Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models. Statist
    • Digital Object Identifier: doi:10.1111/j.1467-9574.2004.00123.x
    • Lok, J., Gill, R., van der Vaart, A. and Robins, J. (2004). Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models. Statist. Neerlandica 58 271-295. Mathematical Reviews (MathSciNet): MR2157006 Digital Object Identifier: doi:10.1111/j.1467-9574.2004.00123.x
    • (2004) Neerlandica , vol.58 , pp. 271-295
    • Lok, J.1    Gill, R.2    van der Vaart, A.3    Robins, J.4
  • 29
    • 84950945692 scopus 로고
    • Model selection and accounting for model uncertainty in graphical models using Occam's window
    • Madigan, D. and Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam's window. J. Amer. Statist. Assoc. 89 1535-1546.
    • (1994) J. Amer. Statist. Assoc , vol.89 , pp. 1535-1546
    • Madigan, D.1    Raftery, A.E.2
  • 31
    • 10844272276 scopus 로고    scopus 로고
    • Propensity score estimation with boosted regression for evaluating causal effects in observational studies
    • McCaffrey, D., Ridgeway, G. and Morral, A. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods 9 403-425.
    • (2004) Psychological Methods , vol.9 , pp. 403-425
    • McCaffrey, D.1    Ridgeway, G.2    Morral, A.3
  • 32
    • 0003229133 scopus 로고    scopus 로고
    • The Bayes net toolbox for MATLAB
    • Murphy, K. P. (2001). The Bayes net toolbox for MATLAB. Comput. Sci. Statist. 33 2001.
    • (2001) Comput. Sci. Statist , vol.33 , pp. 2001
    • Murphy, K.P.1
  • 33
    • 33947230201 scopus 로고    scopus 로고
    • National Highway Traffic Safety Administration, Technical report
    • National Highway Traffic Safety Administration (2007). Traffic safety facts 2007. Technical report.
    • (2007) Traffic Safety Facts 2007
  • 35
    • 1342347558 scopus 로고    scopus 로고
    • Statistics and causal inference: A review
    • Digital Object Identifier: doi:10.1007/BF02595718
    • Pearl, J. (2003). Statistics and causal inference: A review. Test 12 281-318. Mathematical Reviews (MathSciNet): MR2044313 Zentralblatt MATH: 1044.62003 Digital Object Identifier: doi:10.1007/BF02595718
    • (2003) Test , vol.12 , pp. 281-318
    • Pearl, J.1
  • 36
    • 84873497147 scopus 로고    scopus 로고
    • On a class of bias-amplifying covariates that endanger effect estimates. Technical report, Univ. California, Los Angeles
    • Pearl, J. (2009). On a class of bias-amplifying covariates that endanger effect estimates. Technical report, Univ. California, Los Angeles. Mathematical Reviews (MathSciNet): MR2548166
    • (2009) Mathematical Reviews
    • Pearl, J.1
  • 41
    • 0005595058 scopus 로고    scopus 로고
    • On the impossibility of inferring causation from association without background knowledge
    • C. Glymour and G. F. Cooper, eds.), AAAI Press, Menlo Park, CA
    • Robins, J. M. and Wasserman, L. (1999). On the impossibility of inferring causation from association without background knowledge. In Computation, Causation, and Discovery (C. Glymour and G. F. Cooper, eds.) 305-321. AAAI Press, Menlo Park, CA. Mathematical Reviews (MathSciNet): MR1689948
    • (1999) Computation, Causation, and Discovery , pp. 305-321
    • Robins, J.M.1    Wasserman, L.2
  • 42
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • Digital Object Identifier: doi:10.1093/biomet/70.1.41
    • Rosenbaum, P. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70 41-55. Mathematical Reviews (MathSciNet): MR742974 Zentralblatt MATH: 0522.62091 Digital Object Identifier: doi:10.1093/biomet/70.1.41
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.1    Rubin, D.B.2
  • 43
    • 0002531157 scopus 로고
    • Bayesian inference for causal effects: The role of randomization
    • Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Ann. Statist. 6 34-58. Mathematical Reviews (MathSciNet): MR472152 Zentralblatt MATH: 0383.62021 Digital Object Identifier: doi:10.1214/aos/1176344064 Project Euclid: euclid.aos/1176344064
    • (1978) Ann. Statist , vol.6 , pp. 34-58
    • Rubin, D.B.1
  • 44
    • 38249019870 scopus 로고
    • Formal mode of statistical inference for causal effects
    • Rubin, D. B. (1990). Formal mode of statistical inference for causal effects. J. Statist. Plann. Inference 25 279-292.
    • (1990) J. Statist. Plann. Inference , vol.25 , pp. 279-292
    • Rubin, D.B.1
  • 45
    • 84873513707 scopus 로고    scopus 로고
    • Estimation from nonrandomized treatment comparisons using subclassification on propensity scores
    • Rubin, D. B. (1998). Estimation from nonrandomized treatment comparisons using subclassification on propensity scores. Ann. Internal Medicine 8 757-763.
    • (1998) Ann. Internal Medicine , vol.8 , pp. 757-763
    • Rubin, D.B.1
  • 46
    • 14944344423 scopus 로고    scopus 로고
    • Causal inference using potential outcomes: Design, modeling, decisions
    • Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. 100 322-331. Mathematical Reviews (MathSciNet): MR2166071 Zentralblatt MATH: 1117.62418 Digital Object Identifier: doi:10.1198/016214504000001880
    • (2005) J. Amer. Statist. Assoc , vol.100 , pp. 322-331
    • Rubin, D.B.1
  • 47
    • 64349088593 scopus 로고    scopus 로고
    • For objective causal inference, design trumps analysis
    • Rubin, D. B. (2008). For objective causal inference, design trumps analysis. Ann. Appl. Statist. 2 808-840. Mathematical Reviews (MathSciNet): MR2516795 Zentralblatt MATH: 1149.62089 Digital Object Identifier: doi:10.1214/08-AOAS187 Project Euclid: euclid.aoas/1223908042
    • (2008) Ann. Appl. Statist , vol.2 , pp. 808-840
    • Rubin, D.B.1
  • 48
    • 85029379289 scopus 로고    scopus 로고
    • Bayesian causal inference: Approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification
    • (T. O'Hagan and M. West, eds.). Oxford Univ. Press, Oxford
    • Rubin, D. B., Wang, X., Yin, L. and Zell, E. R. (2008). Bayesian causal inference: Approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification. In Handbook of Applied Bayesian Analysis (T. O'Hagan and M. West, eds.). Oxford Univ. Press, Oxford. Mathematical Reviews (MathSciNet): MR2790361
    • (2008) Handbook of Applied Bayesian Analysis
    • Rubin, D.B.1    Wang, X.2    Yin, L.3    Zell, E.R.4
  • 49
    • 57749105474 scopus 로고    scopus 로고
    • Everage causal effects from nonrandomized studies: A practical guide and simulated example
    • Schafer, J. L. and Kang, J. (2008). Everage causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods 13 279-313.
    • (2008) Psychological Methods , vol.13 , pp. 279-313
    • Schafer, J.L.1    Kang, J.2
  • 50
    • 33846683916 scopus 로고    scopus 로고
    • Technical report, Facultad de Ciencias Económicas, Universidad Nacional de Tucumán, San Miguel de Tucumán, Argentina
    • Sfer, A. M. (2005). Randomization and causality. Technical report, Facultad de Ciencias Económicas, Universidad Nacional de Tucumán, San Miguel de Tucumán, Argentina.
    • (2005) Randomization and Causality
    • Sfer, A.M.1
  • 51
    • 0002979137 scopus 로고
    • An algorithm for fast recovery of sparse causal graphs
    • Spirtes, P. and Glymour, C. (1991). An algorithm for fast recovery of sparse causal graphs. Social Science Computer Review 9 62-72.
    • (1991) Social Science Computer Review , vol.9 , pp. 62-72
    • Spirtes, P.1    Glymour, C.2
  • 53
    • 56949093997 scopus 로고    scopus 로고
    • Human error applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems
    • Stanton, N. A. and Salmon, P. M. (2009). Human error applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems. Safety Science 47 227-237.
    • (2009) Safety Science , vol.47 , pp. 227-237
    • Stanton, N.A.1    Salmon, P.M.2
  • 54
    • 33746035971 scopus 로고    scopus 로고
    • The max-min hill-climbing Bayesian network structure learning algorithm
    • Tsamardinos, I., Brown, L. E. and Aliferis, C. F. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65 31-78.
    • (2006) Mach. Learn , vol.65 , pp. 31-78
    • Tsamardinos, I.1    Brown, L.E.2    Aliferis, C.F.3


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