Probabilistic Graphical Model Causal Discovery Latent Causes
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Solution Overview
Problem
Current methods for discovering causal relationships between variables from uncontrolled statistical data struggle to detect latent common causes, limiting their ability to reason about interventions and counterfactuals, especially in scenarios where randomized controlled trials are unethical, expensive, or impractical.
Innovation Solution
A method is developed to generate a probabilistic graphical model that includes causal relationships by extracting a latent variable describing the manifold in two-dimensional data, applying a causal discovery algorithm to determine causal relationships, and identifying latent common causes without requiring extensive training or imposing restrictive assumptions on the underlying causal model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If causal discovery algorithms are applied to uncontrolled statistical data, then causal relationships can be discovered, but latent common causes cannot be detected
Solution Approach 1:
The patent extends the traditional bivariate causal discovery framework to a trivariate framework by introducing a third variable. This dimensional expansion allows the algorithm to detect latent common causes by analyzing the joint distribution of three variables, where the third variable serves as an indicator of potential latent common causes between the first two variables
Solution Approach 2:
The patent introduces a third variable as an intermediary that mediates the detection of latent common causes. This third variable acts as a bridge that connects the first and second variables, allowing the algorithm to infer the presence of latent common causes through the relationships among all three variables
2Measurement precision
If randomized controlled trials are used to discover causal relationships, then accurate causal inference is achieved, but ethical and practical constraints prevent their use in many scenarios
Solution Approach 1:
The patent creates a computational model that copies and simulates the causal discovery process using only observational data. Instead of requiring actual randomized controlled trials, the algorithm creates a probabilistic graphical model that replicates the causal inference capability of RCTs using statistical patterns from uncontrolled data
Solution Approach 2:
The patent changes the parameters and assumptions of causal discovery by moving from purely directed relationships to a framework that incorporates latent common causes. This parameter change allows the algorithm to work with observational data while maintaining causal inference accuracy by adjusting the underlying causal model structure
3Productivity
If existing causal discovery methods are applied, then directed causal relationships can be identified, but the methods cannot distinguish between direct causes and latent common causes
Solution Approach 1:
The patent segments the causal discovery process into distinct analytical steps: first identifying associations between variable pairs, then introducing a third variable to test for latent common causes, and finally constructing the probabilistic graphical model. This segmentation allows the algorithm to systematically distinguish between direct causes and latent common causes
Solution Approach 2:
By adding a third dimension (third variable) to the analysis, the patent enables differentiation between direct causal relationships and latent common causes. The trivariate analysis provides an additional perspective that reveals the underlying causal structure that bivariate analysis cannot detect
Data Source
AI summary
A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.