90% of Neural Networks Can't Explain Their Predictions

Neural networks excel at identifying patterns and correlations, achieving high accuracy in predictions. However, they struggle to provide explanations for their outcomes, leaving users with a “black box” scenario. This issue isn’t limited to neural networks; traditional methods like linear regression and Propensity Score Matching also fall short in explaining causation in complex data sets. This gap is critical when actionable insights are needed for business decisions. Enter Targeted Maximum Likelihood Estimation (TMLE), a method that combines the precision of causal inference with the adaptability of machine learning. TMLE not only satisfies the statistical rigor desired by data scientists but also delivers the business insights that managers crave, bridging the gap between correlation and causation in data analysis.

Source: towardsdatascience.com