A computer implemented method for discovering non-obvious insights from heterogeneous data sources through causality-augmented generative intelligence, the method comprising the steps of:
- acquiring a plurality of heterogeneous data streams including structured data, unstructured textual data, sensor-derived visual data, and graph-encoded relational data;
- preprocessing the heterogeneous data streams by applying schema alignment, metadata harmonization, semantic feature extraction, and temporal index synchronization;
- generating a directed causal knowledge representation by estimating cause-effect relationships among variables present within the heterogeneous data streams using perturbation-based structural causal inference;
- constructing fused latent vectors by integrating causal dependency parameters with semantic feature embeddings derived from the preprocessed data streams;
- synthesizing candidate insights using a generative reasoning process constrained by the directed causal knowledge representation to ensure causal compliance;
- and
- validating the synthesized candidate insights by performing counterfactual assessment against real observational data evidence to retain only insights confirmed to be causally consistent, wherein preprocessing further comprises extracting semantic embeddings for unstructured textual data using a transformer-based language encoder configured to perform multi-level attention layer pooling, and performing visual modality normalization for sensor-derived visual data using illumination-invariant histogram equalization followed by spatial-temporal feature encoding performed through a convolutional recurrent feature extractor having at least two gated recurrent sub-layers with a dropout rate within a range of 0.1-0.3, and further applying graph topology rectification for relational data using Laplacian smoothing with ≤3 smoothing iterations to reduce noise in edge weights while preserving high-order neighborhood structure, the outputs of each preprocessing operation being temporally synchronized through a unified monotonic timestamp alignment protocol in which data points having drift greater than +2% of the reference temporal granularity are rectified via interpolation routines to ensure causal temporal integrity, and wherein estimating cause-effect relationships comprises computing a differentiable structural causal model by applying an acyclicity-constrained optimization procedure using a log-determinant surrogate to enforce directed acyclic graph (DAG) validity, and evaluating structural edge weights through an adaptive likelihood scoring engine wherein each edge is retained only if a Bayesian confidence score exceeds 0.85 and counter-directional influence probability is below 0.1, and further wherein cross-domain causal edges connecting visual-semantic indicators to numerical variables are validated through Shapley-based causal influence attribution computed over at least 5 independent perturbation batches.