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Primary Submission Category: Causal Discovery

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

Authors: Henry Burton,

Presenting Author: Henry Burton*

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 oral presentation will discuss how earthquake engineering research and practice can be transformed by the broad adoption of the language, tools, and models that have been (and continue to be) developed to draw causal conclusions from observational data. Several categories of data-driven earthquake engineering problems that can benefit from causal insights will be discussed. Two widely adopted frameworks from the broader causal inference literature will be examined and linked to hypothetical earthquake engineering problems. The presentation will conclude with a discussion of specific opportunities and challenges toward the widespread use of causal inference as a tool for knowledge discovery in earthquake engineering.