Microsoft patents LLM tool to find hidden factors in data analysis
Microsoft describes a system that uses a large language model to identify hidden variables, known as confounders, that might skew the results of a data study. The software generates test scenarios to check if these variables actually affect the outcome, then removes any that do not meet a statistical significance threshold. Finally, it creates a report listing the confirmed factors and their importance.
In the filing’s own words
A data processing system implements analyzing input data from analytics platform using a large language model to cause the model to analyze the metadata and generate a list of causal confounders that may influence a causal relationship between an independent variable and an output being studied; generating a plurality of causal hypothesis scenarios testing how one or more causal confounders of the list of causal confounders influence a relationship between the independent variable and the output being studied; executing the plurality of causal hypothesis scenarios to obtain causal inference results; performing statistical testing on the causal results to determine whether each confounder of the list of causal confounders satisfies a significance threshold; pruning the list of causal confounders to remove confounders that do not satisfy the significance threshold; and generating a report that identifies each of the confounders and includes a description of a significance of each of the confounders.
What exactly they claim (claim 1)
1. A data processing system comprising: a processor; and a memory storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations of: receiving input data from an analytics platform, the input data including metadata associated with the input data that describes characteristics and properties of the input data; constructing a prompt to a large language model based on the metadata, the prompt instructing the large language model to analyze the metadata and generate a list of candidate causal confounders that may influence a causal relationship between an independent variable and an output being studied; providing the prompt as an input to the large language model to obtain the list of candidate causal confounders; generating a plurality of causal hypothesis scenarios, each causal hypothesis scenario testing how one or more causal confounders of the list of candidate causal confounders influence a relationship between the independent variable and the output being studied; executing the plurality of causal hypothesis scenarios to obtain causal inference results; performing statistical testing on the causal inference results to determine whether each confounder of the list of candidate causal confounders satisfies a significance threshold; iteratively pruning the list of candidate causal confounders to remove confounders that do not satisfy the significance threshold to generate a pruned list of causal confounders; and generating a report based on the pruned list of causal confounders that identifies each of the confounders and includes a description of a significance of each of the confounders.
Facts
- Company
- Microsoft (Microsoft Technology Licensing, LLC)
- Official title
- LARGE LANGUAGE MODEL INFERRED CAUSAL CONFOUNDER GENERATION
- Publication number
- US20260268091A1
- Application number
- 19073169
- Filed
- Mar 7, 2025
- Published
- Sep 10, 2026
- Inventors
- 3
- Claims
- 20
Classified under
G06F 40/40 Physics & computingG06F 9/544 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.