ELT Pipelines Enhanced with Causal Intelligence for Reliable and Bias-Resilient AI Decision Systems
In the world of modern analytics and machine learning, pipelines known as Extract-Load-Transform (ELT) pipelines are commonly used to transform raw data into representations that are suitable for models. But most of the existing ELT workflows are primarily correlation driven and don’t explicitly maintain causality during data transformation. This means that joins, filtering, aggregation and feature preparation steps can create confounding effects, selection bias, or false treatment-outcome relationship that can create unreliable AI-based decisions. This paper proposes such a causal intelligence enhanced ELT framework, where causal reasoning is integrated into the ELT process directly. Three pipeline-native components are proposed: causal graph-constrained data integration via Causal Join, covariate balancing and adjustment in-pipeline via Propensity Balance, and execution optimization via task scheduling and causal artifact reuse via Causal Scheduler. The proposed method is reduces bias earlier in the data lifecycle compared with purely post-hoc causal modelling less biased than post hoc causal modelling approaches as the transformation process itself is less biased. On experiments performed on the IHDP, Twins and Criteo uplift datasets, we have seen improved estimation of treatment effects, lower bias and higher predictive reliability. The proposed framework reduces the error in the estimation by approximately 55-65%, improves predictive accuracy from 82.0% to 91.0%, corresponding to a 9 percentage-point improvement. and gives a $7.5 \times$ speedup performance for GPU compared to CPU. These findings point towards the promise of causal intelligence in making more reliable and efficient decisions within ELT pipelines, potentially enhancing the overall reliability and trustworthiness of AI-driven decision systems in the field.