Causality, Causal Discovery, and Causal Inference in Structural Engineering

by   M. Z. Naser, et al.

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that we might have. This paper builds a case for causal discovery and causal inference and contrasts that against traditional machine learning approaches; all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.


page 1

page 3

page 10

page 12

page 16

page 20

page 21

page 22


Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge

Experiments remain the gold standard to establish an understanding of fi...

A Survey on Causal Discovery: Theory and Practice

Understanding the laws that govern a phenomenon is the core of scientifi...

Simulation Experiments as a Causal Problem

Simulation methods are among the most ubiquitous methodological tools in...

CausalOps – Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models

Causal probabilistic graph-based models have gained widespread utility, ...

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena

Current work on using visual analytics to determine causal relations amo...

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Human nonverbal emotional communication in dyadic dialogs is a process o...

Please sign up or login with your details

Forgot password? Click here to reset