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Estimating Heterogeneity in Travel Mode Choice Shifts with Causal Forests

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  • Rishabh Singh Chauhan
  • Mahdi Ghadimi
  • Lishun Liu

Abstract

Objectives: While causal analysis of travel behavior is an emerging field, estimating heterogeneity in mode choice through causal modeling remains unexplored. This study demonstrates the application of a novel causal method, causal forest, to quantify the heterogeneity in travel mode choice shifts caused by the COVID-19 pandemic. Methods: We applied causal forests, a non-parametric causal machine learning method, to 802,935 trip records from the 2017 and 2022 waves of the National Household Travel Survey. The 2017 wave serves as the pre-pandemic control group, while the 2022 wave represents the treatment condition. Within the potential outcomes framework, we estimate average treatment effects (ATE), heterogeneous treatment effects (HTE), and conditional average treatment effects (CATE) across diverse socio-demographic groups and trip characteristics. Findings: Our results reveal an estimated ATE of a 1.86 percentage point (pp) increase in car-mode share, contrasted with decreases of 0.38 pp and 1.57 pp in public transit and walking, respectively. The largest increases in car use appeared for short-distance trips (one mile or less), households with annual incomes exceeding USD 200,000, and female travelers. Novelty: This is one of the first applications of causal forests to travel mode choice, and the first to use causal machine learning to estimate the pandemic's causal effect on mode choice analysis. Practical Applications: This study discusses methodological advantages, inherent assumptions, and limitations of causal forests within the context of transportation planning. This methodology is applied to COVID-19 travel data to illustrate how causal heterogeneity analysis can offer a deeper understanding of changes in mode choice. These insights are valuable for planners and policymakers in making policies related to mode shifts under an intervention.

Suggested Citation

  • Rishabh Singh Chauhan & Mahdi Ghadimi & Lishun Liu, 2026. "Estimating Heterogeneity in Travel Mode Choice Shifts with Causal Forests," Papers 2608.04208, arXiv.org.
    • Handle: RePEc:arx:papers:2608.04208
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    1. Kevin Blattler & Hannes Wallimann & Widar von Arx, 2024. "Free public transport to the destination: A causal analysis of tourists' travel mode choice," Papers 2401.14945, arXiv.org, revised Feb 2024.
    2. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, June.
    3. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    4. Blättler, Kevin & Wallimann, Hannes & von Arx, Widar, 2024. "Free public transport to the destination: A causal analysis of tourists’ travel mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).
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