Publications

2025

1. 

   Do Large Language Models Reason Causally Like Us? Even Better?

   Hanna M. Dettki, Brenden M. Lake, Charley M. Wu, and Bob Rehder

   In arXiv preprint, 2025

   Abs arXiv Bib PDF Code

   Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether their responses reflect true understanding or statistical patterns. We compared causal reasoning in humans and four LLMs using tasks based on collider graphs, rating the likelihood of a query variable occurring given evidence from other variables. We find that LLMs reason causally along a spectrum from human-like to normative inference, with alignment shifting based on model, context, and task. Overall, GPT-4o and Claude showed the most normative behavior, including "explaining away", whereas Gemini-Pro and GPT-3.5 did not. Although all agents deviated from the expected independence of causes - Claude the least - they exhibited strong associative reasoning and predictive inference when assessing the likelihood of the effect given its causes. These findings underscore the need to assess AI biases as they increasingly assist human decision-making.

   @inproceedings{dettki2025largelanguagemodelsreason,
     title = {Do Large Language Models Reason Causally Like Us? Even Better?},
     author = {Dettki, Hanna M. and Lake, Brenden M. and Wu, Charley M. and Rehder, Bob},
     booktitle = {arXiv preprint},
     year = {2025},
   }

2024

1. 

   Do Large Language Models Understand Cause and Effect?

   Hanna M. Dettki, Charley Wu, Brenden Lake, and Bob Rehder

   In WiML Workshop at NeurIPS, 2024

   Bib PDF

   @inproceedings{dettki2024dolarge,
     title = {Do Large Language Models Understand Cause and Effect?},
     author = {Dettki, Hanna M. and Wu, Charley and Lake, Brenden and Rehder, Bob},
     booktitle = {WiML Workshop at NeurIPS},
     year = {2024},
   }

2. 

   Plenoptic: A platform for synthesizing model-optimized visual stimuli

   Edoardo Balzani, Kathryn Bonnen, William Broderick, Hanna M. Dettki, Lyndon Duong, and 6 more authors

   2024

   Bib Code Poster

   @software{plenoptic,
     author = {Balzani, Edoardo and Bonnen, Kathryn and Broderick, William and Dettki, Hanna M. and Duong, Lyndon and Fiquet, Pierre-Étienne and Herrera-Esposito, Daniel and Parthasarathy, Nikhil and Simoncelli, Eero and Yerxa, Thomas and Zhao, Xinyuan},
     doi = {10.5281/zenodo.10151130},
     license = {MIT},
     title = {{Plenoptic: A platform for synthesizing model-optimized visual stimuli}},
     year = {2024},
   }

2019

1. 

   Executable State Machines Derived from Structured Textual Requirements-Connecting Requirements and Formal System Design

   Benedikt Walter, Jan Martin, Jonathan Schmidt, Hanna M. Dettki, and Stephan Rudolph

   In Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development, 2019

   Abs Bib HTML PDF

   There exists a gap between (textual) requirements specification and systems created in the system design process. System design, particular in automotive, is a tremendously complex process. The sheer number of requirements for a system is too high to be considered at once. In industrial contexts, complex systems are commonly created through many design iterations with numerous hardware samples and software versions build. System experts include many experience-based design decisions in the process. This approach eventually leads to a somewhat consistent system without formal consideration of requirements or a traceable design decision process. The process leaves a de facto gap between specification and system design. Ideally, requirements constrain the initial solution space and system design can choose between the design variants consistent with that reduced solution space. In reality, the true solution space is unknown and the effect of particular requirements on that solution space is a guessing game. Therefore, we want to propose a process chain that formally includes requirements in the system design process and generates an executable system model. Requirements documented as structured text are mapped into the logic space. Temporal logic allows generation of consistent static state machines. Extracting and modelling input/output signals of that state machine enables us to generate an executable system model, fully derived from its requirements. This bridges the existing gap between requirements specification and system design. The correctness and usefulness of this approach is shown in a case study on automotive systems at Daimler AG.

   @inproceedings{walter2019executable,
     title = {Executable State Machines Derived from Structured Textual Requirements-Connecting Requirements and Formal System Design},
     author = {Walter, Benedikt and Martin, Jan and Schmidt, Jonathan and Dettki, Hanna M. and Rudolph, Stephan},
     booktitle = {Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development},
     pages = {193--200},
     year = {2019},
   }