Two-Stage Structural Causal Modeling for Action Effects
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Solution Overview
Problem
Conventional methods for learning structural causal models (SCMs) are computationally expensive due to the combinatorial nature of possible causal structures and the difficulty of inferring causal mechanisms, making them inefficient and limited to specific domains.
Innovation Solution
A two-stage approach is employed, where a first machine learning model infers a causal order of variables, followed by training a structural causal model using the predicted ordering, leveraging a transformer-based auto-encoder architecture to encode causal relationships efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to learn structural causal models, then causal inference can be performed, but computational cost becomes excessively high
Solution Approach 1:
The patent segments the causal model learning process into two distinct stages: (1) a machine learning model infers a causal ordering of variables from observational data, and (2) a structural causal model is trained using this pre-computed ordering. This segmentation transforms the computationally intensive combinatorial search into a more efficient two-stage process, reducing overall computational cost while maintaining causal inference reliability.
Solution Approach 2:
The patent applies preliminary action by using a machine learning model to pre-infer the causal ordering of variables before training the structural causal model. This preliminary causal ordering computation simplifies the subsequent SCM training process, avoiding the need to search through all possible causal structures and significantly reducing computational requirements.
2Measurement precision
If conventional methods are used to learn structural causal models, then causal relationships can be inferred, but the process becomes difficult and time-consuming
Solution Approach 1:
The patent divides the causal discovery process into two stages: first inferring causal ordering using machine learning on observational data, then training the SCM using this pre-established ordering. This segmentation dramatically reduces training time while preserving the precision of causal relationship detection by leveraging the causal ordering information.
Solution Approach 2:
The patent performs preliminary causal ordering inference using machine learning models before training the structural causal model. This preliminary action establishes the causal structure in advance, transforming the training process into a more efficient procedure that requires significantly less time while maintaining measurement precision for causal relationships.
3Reliability
If conventional methods are used to learn structural causal models, then domain-specific causal structures can be captured, but adaptability to new domains is limited
Solution Approach 1:
The patent creates a universal approach by using a machine learning model to infer causal ordering that can be applied across different domains. The same two-stage framework works for various domains (healthcare, genetics, manufacturing, engineering), allowing the SCM to capture domain-specific causal structures while maintaining adaptability through the generalizable causal ordering inference mechanism.
Data Source
AI summary
Example embodiments described herein provide a two-stage approach for training, on a dataset of samples received as input, a structural causal model (SCM). In a first stage of the example two-stage approach, a trained causal ordering predictor is used to infer a causal order of variables from the dataset. In a second stage, the SCM is trained on the same dataset using the predicted causal ordering from the first stage. Once trained, the SCM may be used to predict a causal effect of an action on a target system.