Counterfactuals for Design: A Model-Agnostic Method For Design
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Authors: name: Lyle Regenwetter, name: Yazan Abu Obaideh, name: Faez Ahmed

We introduce Multi-Objective Counterfactuals for Design (MCD), a novel method
for counterfactual optimization in design problems. Counterfactuals are
hypothetical situations that can lead to a different decision or choice. In
this paper, the authors frame the counterfactual search problem as a design
recommendation tool that can help identify modifications to a design, leading
to better functional performance. MCD improves upon existing counterfactual
search methods by supporting multi-objective queries, which are crucial in
design problems, and by decoupling the counterfactual search and sampling
processes, thus enhancing efficiency and facilitating objective tradeoff
visualization. The paper demonstrates MCD's core functionality using a
two-dimensional test case, followed by three case studies of bicycle design
that showcase MCD's effectiveness in real-world design problems. In the first
case study, MCD excels at recommending modifications to query designs that can
significantly enhance functional performance, such as weight savings and
improvements to the structural safety factor. The second case study
demonstrates that MCD can work with a pre-trained language model to suggest
design changes based on a subjective text prompt effectively. Lastly, the
authors task MCD with increasing a query design's similarity to a target image
and text prompt while simultaneously reducing weight and improving structural
performance, demonstrating MCD's performance on a complex multimodal query.
Overall, MCD has the potential to provide valuable recommendations for
practitioners and design automation researchers looking for answers to their
``What if'' questions by exploring hypothetical design modifications and their
impact on multiple design objectives. The code, test problems, and datasets
used in the paper are available to the public at
decode.mit.edu/projects/counterfactuals/.