Causal Impact Evaluation of Transport Interventions: An Overview of Methods, Applied Scenarios, and Recent Advances
Yingheng Zhang & Hui Li
What the paper says
Due to their clear causal interpretability, the potential outcome framework and complementary statistical and econometric methods for causal inference are increasingly applied to ex-post evaluation of a diverse variety of transport interventions. We attempt to present an overview of these methods from a practitioner’s perspective. We start by describing the core concepts and notations of statistical causality in quantitative evaluation tasks, then summarise established analytic techniques. During this process, we introduce their backgrounds and basic ideas, and discuss transport applications. References to more technical issues and recent advances are provided accordingly. Lastly, we outline several challenges and future research directions.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.