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Intelligent Reasoning Lab (Shimizu Lab)
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A data science and AI lab that investigates causal relationships in observational data.
AboutAbout the lab
Uncovering causal relationships from observational data
The Intelligent Reasoning Lab (Shimizu Lab) is a data science and AI lab focused on causal discovery.
It aims to make causal discovery a standard analytical method in scientific research and in industry and policy settings, building the foundation for a society where causal inference is used routinely.
The lab works with researchers and practitioners from a wide range of fields, from the natural and social sciences to engineering and medicine.
In particular, it develops new methodologies that can estimate causal relationships from observational data even in situations where interventional experiments are difficult.
Key terms
- Causal discovery
- Methods for estimating causal relationships from observational data. This is the main area of research in this lab.
- Observational data
- Data that records what happened as it happened, without manipulating conditions in an experiment
- Interventional experiment
- An experiment in which researchers change a condition thought to be the cause and examine the resulting difference in outcomes
ResearchResearch themes
The lab aims to make causal discovery a standard analytical method in scientific research and in industry and policy settings, and to build the foundation for a society where causal inference is used routinely. To this end, it studies mathematical methodologies for uncovering causal relationships, in collaboration with researchers and practitioners from various fields.
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MembersFaculty
The members of the Intelligent Reasoning Lab (Shimizu Lab) are faculty, researchers, visiting faculty and researchers, students in the master's program and undergraduate students, administrative assistants, and technical assistants.
- ProfessorShohei Shimizu
- Associate ProfessorPHAM Thong
- Assistant ProfessorHiroshi Takeuchi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Domino” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Methods for estimating causation from observational data: “Red Light Intersection ( )” Dave Lauretti from Burlington, Canada / CC BY 2.0 (cropped)
- Related lab (Advanced Electronic Devices Lab): “PlasticLogic flexible display” Steve Jurvetson / CC BY 2.0 (cropped)
- Related lab (Integrated Intelligent Media Lab): “Artificial Worldviews Mapping ChatGPT” Kimfalbrecht / CC BY-SA 4.0 (cropped)
- Related lab (Knowledge Science Lab (Komatani Lab)): “Arxiu audio” Jordi R. / CC BY 3.0 (cropped)
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