# Causal Inference in Travel Demand Modeling (and the lack thereof)

## TL;DR

This paper discusses the disconnect between travel demand modeling and causal inference, emphasizing the need for integrating causal methods into travel demand studies to improve validity and decision-making.

## Contribution

It highlights the importance of causal inference in travel demand modeling and encourages interdisciplinary dialogue without providing specific solutions.

## Key findings

- Identifies a gap between travel demand modeling and causal inference.
- Suggests potential benefits of integrating causal methods into travel demand studies.
- Calls for increased interdisciplinary collaboration and discussion.

## 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.