title: Counterfactuals for Design: A Model-Agnostic Method For Design
    Recommendations
summary: 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/.
paper_authors._id: 646aca11ca17a49700e88c52
paper_authors.name: Lyle Regenwetter
paper_authors._id: 646aca11ca17a49700e88c53
paper_authors.name: Yazan Abu Obaideh
paper_authors._id: 646aca11ca17a49700e88c54
paper_authors.name: Faez Ahmed
paper_title: Counterfactuals for Design: A Model-Agnostic Method For Design
    Recommendations
paper_summary: 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/.
paper_ai_keywords: Multi-Objective Counterfactuals for Design (MCD) counterfactuals counterfactual search labels recommendation tool multi-objective queries decoupling Counterfactuals search and sampling Discriminative performance Functional performance Structural safety factor language model subjective text prompt multimodal query design automation researchers ``What if'' questions hypothetical design modifications multiple design objectives