Causal inference on observational data: Opportunities and challenges in earthquake engineering

Abstract

Collecting and analyzing observational data are essential to learning and implementing lessons in earthquake engineering. Historically, the methods that have been used to analyze and draw conclusions from empirical data have been limited to traditional statistics. The models developed using these techniques are able to capture associative relationships between important variables. However, the intervention decisions geared toward seismic risk mitigation should ideally be informed by an understanding of the causal mechanisms that drive infrastructure performance and community response. This article advocates for a paradigm shift in earthquake engineering where the language, tools, and models that have been (and continue to be) developed to draw causal conclusions from observational data are adopted. Several categories of data-driven earthquake engineering problems that can benefit from causal insights are examined. Two widely adopted frameworks from the broader causal inference literature are presented and linked to hypothetical earthquake engineering problems. The critical role of semi-parametric models and sensitivity analysis in justifying causal claims is also discussed. The article concludes with a discussion of specific opportunities and challenges toward the widespread use of causal inference as a tool for knowledge discovery in earthquake engineering. The ability to leverage the underlying physics of a problem within a causal inference framework is identified as both an opportunity and challenge for earthquake engineering researchers.