Causal Learning Model for Reducing Spurious Correlation Bias
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Solution Overview
Problem
Traditional deep learning systems face accuracy issues due to spurious data points in training data, leading to biases and reduced performance when applied to external data sets not used for training.
Innovation Solution
A method involving a generative model that determines attribute values, generates sensitivity maps, and creates causal graphs to differentiate between causal relationships and correlations, enabling the use of learned causal relationships for accurate predictions in external data sets with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional deep learning systems make statistical correlations between data points in training data, then the model can be trained efficiently, but spurious data points lead to biases and reduced accuracy when applied to external data sets
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) training a generative model on the training data set to learn the data distribution, and (2) using the trained generative model to generate synthetic data points that augment the training data. This segmentation allows the system to leverage the efficiency of traditional deep learning while mitigating its biases through causal structure learning from the generated data.
Solution Approach 2:
The patent introduces a causal structure learning module as an intermediary between the generative model and the final prediction system. This intermediary analyzes the synthetic data generated by the generative model to discover causal relationships, which then guide the training of prediction models. This intermediary layer filters out spurious correlations and focuses on genuine causal patterns, improving reliability on external data sets.
2Adaptability or versatility
If the model learns from training data with spurious data points, then the model can be trained on available data, but biases are introduced that reduce accuracy on external data sets
Solution Approach 1:
The patent applies preliminary action by first training the generative model on the available training data to learn the underlying data distribution before any causal analysis is performed. This preliminary training step ensures that the generative model captures the true data patterns, and subsequent causal structure learning is performed on synthetic data that reflects these patterns without the biases present in the original training data.
Solution Approach 2:
The patent changes the parameter space by moving from learning directly from observed training data to learning causal structures from synthetic data generated by the generative model. This parameter change allows the system to maintain adaptability to available data while improving reliability, as the causal structure is inferred from a different data distribution that excludes spurious correlations.
3Measurement precision
If sensitivity maps are generated based on differences in attribute values from latent space dimension changes, then causal relationships can be differentiated from correlations, but the process requires additional computational steps
Solution Approach 1:
The patent applies self-service by using the generative model's own latent space representations to generate the sensitivity maps. The latent space dimensions, which already encode the data distribution, are perturbed and fed back into the generative model to observe changes in attribute values. This self-referential approach allows the system to differentiate causal relationships without requiring external annotations or additional complex measurement apparatus.
Solution Approach 2:
The patent performs partial action by focusing the sensitivity analysis only on the latent space dimensions that are most relevant to the attributes of interest. Rather than analyzing all possible data variations, the system selectively perturbs specific latent dimensions and measures their impact on key attributes, reducing computational complexity while maintaining measurement precision for the most important causal relationships.
Data Source
AI summary
Techniques are provided for causal learning and prediction. In one embodiment, the techniques involve determining a first attribute value based on an output of a generative model trained on a first data set, generating a second attribute value based on a change of a latent space dimension of the generative model, generating a sensitivity map based on a difference between the first attribute value and the second attribute value; and generating a causal graph based on the sensitivity map.