A two-dimensional propensity score matching method for longitudinal quasi-experimental studies: A focus on travel behavior and the built environment

Abstract

The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials and match observations in both dimensions to find synthetic control groups that are similar to the treatment group and to match subjects across before- and after-treatment periods. We call this a two-dimensional propensity score matching method. This method demonstrates superior performance for improving treatment effect estimation based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the treatment effect of transportation infrastructure on travel behavior.

Keywords

Built environment travel behavior causal inference propensity score matching

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References

Austin

(2011) Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics 10(2): 150–161.

Boarnet

Hong

Lee

, et al. (2013) The Exposition light rail line study: A before and after study of the impact of new light rail transit service.

Cao

(2010) Exploring causal effects of neighborhood type on walking behavior using stratification on the propensity score. Environment and Planning A: Economy and Space 42(2): 487–504.

Cao

Fan

(2012) Exploring the influences of density on travel behavior using propensity score matching. Environment and Planning B: Planning and Design 39(3): 459.

Cao

Fan

(2010) Exploring the connections among residential location, self-selection, and driving: Propensity score matching with multiple treatments. Transportation Research Part A: Policy and Practice 44(10): 797–805.

Chatman

(2014) Estimating the effect of land use and transportation planning on travel patterns: Three problems in controlling for residential self-selection. Journal of Transport and Land Use 7(3): 47–56.

d’Agostino

(1998) Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine 17(19): 2265–2281.

Dehejia

Wahba

(2002) Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics 84(1): 151–161.

Rosenbaum

(1993) Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics 2(4): 405–420.

10.

Guan

Wang

Jason Cao

(2020) The role of residential self-selection in land use-travel research: a review of recent findings. Transport Reviews 40(3): 267–287.

11.

Guo

Fraser

(2014) Propensity Score Analysis: Statistical Methods and Applications. London: Sage Publications.

12.

Heinen

Mackett

van Wee

, et al. (2018) Residential self-selection in quasi-experimental and natural experimental studies. Journal of Transport and Land Use 11(1): 939–959.

13.

Imai

King

, et al. (2007) Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15(3): 199–236.

14.

King

Nielsen

(2016) Why propensity scores should not be used for matching. Copy at http://j. mp/1sexgVw Download Citation BibTex Tagged XML Download Paper, 378.

15.

Kramer

(1983) The ecological fallacy revisited: Aggregate-versus individual-level findings on economics and elections, and sociotropic voting. The American Political Science Review 77: 92–111.

16.

Lewis

(1973) Counterfactuals, Library of Philosophy and Logic. Oxford: Basil Blackwell.

17.

Mokhtarian

Cao

(2008) Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Transportation Research Part B: Methodological 42(3): 204–228.

18.

Mokhtarian

van Herick

(2016) Quantifying residential self-selection effects: A review of methods and findings from applications of propensity score and sample selection approaches. Journal of Transport and Land Use 9(1): 9–28.

19.

Oakes

Johnson

(2006) Propensity score matching for social epidemiology. Methods in Social Epidemiology 1: 370–393.

20.

Rosenbaum

Rubin

(1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 39(1): 33–38.

21.

Rubin

(1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66(5): 688.

22.

Rubin

(1977) Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics 2(1): 1–26.

23.

Rubin

(2005) Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association 100(469): 322–331.

24.

Smith

Todd

(2001) Reconciling conflicting evidence on the performance of propensity-score matching methods. American Economic Review 91(2): 112–118.

25.

Spears

Boarnet

Houston

(2016) Driving reduction after the introduction of light rail transit: Evidence from an experimental-control group evaluation of the Los Angeles Expo Line. Urban Studies 0042098016657261.

26.

Thoemmes

Kim

(2011) A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research 46(1): 90–118.

27.

Wang

Lin

(2019) Built environment, travel behavior, and residential self-selection: A study based on panel data from Beijing. Transportation 46(1): 51–74.

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