Microsoft patents method to find cause-and-effect in sparse data
This filing describes a computer method that combines real-world data with simulated data to build a map of cause-and-effect relationships. It uses neural networks to process both data types and iteratively refines a causal graph to predict outcomes in new situations.
In the filing’s own words
This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.
What exactly they claim (claim 1)
1. Computer-readable storage media encoding instructions that cause a processor to perform operations comprising: obtaining real data; generating simulated data with one or more simulators; learning a first hidden embedding of the real data using a first neural network; learning a second hidden embedding of the simulated data using a second neural network; iteratively revising the first hidden embedding, the second hidden embedding, and a causal graph, wherein the iteratively revising comprises passing the first hidden embedding learned from the first neural network to the causal graph and the second hidden embedding learned from the second neural network to the causal graph; and learning the causal graph through the iterative revisions.
Facts
- Company
- Microsoft (MICROSOFT TECHNOLOGY LICENSING, LLC)
- Official title
- DATA DRIVEN APPROACHES TO IMPROVE UNDERSTANDING OF PROCESS-BASED MODELS AND DECISION MAKING
- Publication number
- US20260289241A1
- Application number
- 19672551
- Filed
- May 8, 2026
- Published
- Sep 24, 2026
- Inventors
- 6
- Claims
- 20
Classified under
G06N 3/0455 Physics & computingG06N 7/01 Physics & computingMore from Microsoft in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.