Causal inference to detect selection bias in road safety epidemiology

TL;DR

This paper examines the potential for bias in responsibility analyses within road safety studies, using causal inference frameworks to determine when causal effects can be accurately estimated from observational data.

Contribution

It applies structural causal models to responsibility analyses, revealing limitations in estimating causal odds-ratios due to bias introduced by severity-related factors.

Findings

Causal odds-ratios are not estimable from responsibility analyses when severity is affected by speed.

Responsibility analyses may be biased when severity influences both responsibility and the factors studied.

Numerical simulations demonstrate the magnitude of bias and limitations in real data applications.

Abstract

In the field of road safety, it is common to use responsibility analyses to assess the effect of a given factor on the risk of being responsible for an accident, among drivers involved in an accident only. Even if this design is now widely adopted in the field, the question of selection bias is often raised. The structural Causal Model framework now provides valuable tools to assess causal effects from observational data and identify selection bias. In this article, we briefly review recent results regarding the recoverability of causal effects from selection biased data, and apply them to the case of responsibility analyses. Our objective is to formally determine whether causal effects can be unbiasedly estimated through this type of analyses, when available data are restricted to severe accidents, as it is commonly the case in practice. However, because speed has a direct effect on the severity of the accident, we show that causal odds-ratios are not estimable from responsibility analyses. We present numerical results to illustrate our argument, the magnitude of the bias and to discuss recent results from real data.