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@article{Graham:2025:10.1016/j.tra.2024.104324,
author = {Graham, DJ},
doi = {10.1016/j.tra.2024.104324},
journal = {Transportation Research Part A: Policy and Practice},
title = {Causal inference for transport research},
url = {http://dx.doi.org/10.1016/j.tra.2024.104324},
volume = {192},
year = {2025}
}
TY - JOUR
AB - This paper provides a consolidated overview of the statistical literature on causal inference, emphasising its relevance and applicability for transportation research. It outlines a framework for causal identification based on the concept of potential outcomes and provides a summary of core contemporary methods that can be used for estimation. Typical challenges encountered in identifying cause–effect relationships in applied transportation research are analysed via case study simulations, and R code to execute and adapt causal estimators is made available. Causal inference can be used to obtain unbiased and consistent estimates of causal effects in non-experimental settings when interventions or exposures are non-randomly assigned. The paper argues that empirical analyses in transport research are typically conducted in this setting, and consequently, that causal inference has immediate and valuable applicability.
AU - Graham,DJ
DO - 10.1016/j.tra.2024.104324
PY - 2025///
SN - 0965-8564
TI - Causal inference for transport research
T2 - Transportation Research Part A: Policy and Practice
UR - http://dx.doi.org/10.1016/j.tra.2024.104324
VL - 192
ER -
Transport Strategy Centre
Department of Civil and Environmental Engineering
South Kensington Campus
Imperial College London
London SW7 2AZ - UK
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