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Causal inference in travel demand modeling (and the lack thereof)

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  • Brathwaite, Timothy
  • Walker, Joan L.

Abstract

This paper is about the general disconnect that we see, both in practice and in literature, between the disciplines of travel demand modeling and causal inference. In this paper, we assert that travel demand modeling should be one of the many fields that focuses on the production of valid causal inferences, and we hypothesize about reasons for the current disconnect between the two bodies of research. Furthermore, we explore the potential benefits of uniting these two disciplines. We consider what travel demand modeling can gain from greater incorporation of techniques and perspectives from the causal inference literatures, and we briefly discuss what the causal inference literature might gain from the work of travel demand modelers. In this paper, we do not attempt to “solve” issues related to the drawing of causal inferences from travel demand models. Instead, we hope to spark a larger discussion both within and between the travel demand modeling and causal inference literatures. In particular, we hope to incite discussion about the necessity of drawing causal inferences in travel demand applications and the methods by which one might credibly do so.

Suggested Citation

  • Brathwaite, Timothy & Walker, Joan L., 2018. "Causal inference in travel demand modeling (and the lack thereof)," Journal of choice modelling, Elsevier, vol. 26(C), pages 1-18.
  • Handle: RePEc:eee:eejocm:v:26:y:2018:i:c:p:1-18
    DOI: 10.1016/j.jocm.2017.12.001
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    as
    1. Jordan Louviere & Kenneth Train & Moshe Ben-Akiva & Chandra Bhat & David Brownstone & Trudy Cameron & Richard Carson & J. Deshazo & Denzil Fiebig & William Greene & David Hensher & Donald Waldman, 2005. "Recent Progress on Endogeneity in Choice Modeling," Marketing Letters, Springer, vol. 16(3), pages 255-265, December.
    2. Marsden, Greg & Docherty, Iain, 2013. "Insights on disruptions as opportunities for transport policy change," Transportation Research Part A: Policy and Practice, Elsevier, vol. 51(C), pages 46-55.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    4. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    5. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    6. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    7. Golob, Thomas F., 2003. "Structural equation modeling for travel behavior research," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 1-25, January.
    8. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part II," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-33, February.
    9. Dagsvik, John K, 2017. "Invariance Axioms and Functional Form Restrictions in Structural Models," Memorandum 08/2017, Oslo University, Department of Economics.
    10. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    11. Andre Carrel & Raja Sengupta & Joan L. Walker, 2017. "The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker," Transportation, Springer, vol. 44(4), pages 643-679, July.
    12. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    13. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    14. Tikka, Santtu & Karvanen, Juha, 2017. "Identifying Causal Effects with the R Package causaleffect," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i12).
    15. Keele, Luke, 2015. "The Statistics of Causal Inference: A View from Political Methodology," Political Analysis, Cambridge University Press, vol. 23(3), pages 313-335, July.
    16. Steffen L. Lauritzen & Thomas S. Richardson, 2002. "Chain graph models and their causal interpretations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 321-348, August.
    17. Abbas, Khaled A. & Bell, Michael G. H., 1994. "System dynamics applicability to transportation modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 28(5), pages 373-390, September.
    18. Eleanor McDonnell Feit & Mark A. Beltramo & Fred M. Feinberg, 2010. "Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes," Management Science, INFORMS, vol. 56(5), pages 785-800, May.
    19. Agyemang-Duah, Kwaku & Hall, Fred L., 1997. "Spatial transferability of an ordered response model of trip generation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(5), pages 389-402, September.
    20. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
    21. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    22. Min Ding & Rajdeep Grewal & John Liechty, 2005. "Incentive-aligned conjoint analysis," Framed Field Experiments 00139, The Field Experiments Website.
    23. Fifer, Simon & Rose, John M., 2016. "Can you ever be certain? Reducing hypothetical bias in stated choice experiments via respondent reported choice certaintyAuthor-Name: Beck, Matthew J," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 149-167.
    24. James J. Heckman, 2000. "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, Oxford University Press, vol. 115(1), pages 45-97.
    25. Chung, Yi-Shih & Chiou, Yu-Chiun, 2017. "Willingness-to-pay for a bus fare reform: A contingent valuation approach with multiple bound dichotomous choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 289-304.
    26. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    27. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    28. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    29. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
    30. Abdul Pinjari & Ram Pendyala & Chandra Bhat & Paul Waddell, 2011. "Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions," Transportation, Springer, vol. 38(6), pages 933-958, November.
    31. Leontief, Wassily, 1971. "Theoretical Assumptions and Nonobserved Facts," American Economic Review, American Economic Association, vol. 61(1), pages 1-7, March.
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