Causal Analysis & Econometrics

Service Description

Causal analysis is essential when the question is not “what correlates?” but “what truly causes change?”. This service applies econometric and statistical methods to uncover causal effects and quantify their impact, enabling data-driven policy, business, and investment decisions that rest on empirical credibility. With a PhD in Economics focused on large-scale banking and employment data, I have extensive experience in using Difference-in-Differences, Instrumental Variables, Regression Discontinuity, and panel models to identify causal relationships. These methods allow us to separate true effects from confounding factors — a key challenge in complex systems. I combine classical econometric frameworks with modern data science techniques such as causal forests, double machine learning, and treatment effect estimation in addition to classic A/B testing. This hybrid approach merges interpretability with predictive power, making it suitable for applied business contexts where both explanation and accuracy matter. Projects may include evaluating policy interventions, assessing credit programs, or testing the impact of business initiatives. Each analysis involves clear identification strategies, transparent assumptions, and statistical validation to ensure robustness. Clients value this service for its emphasis on clarity: I translate technical findings into actionable narratives that guide strategy and decision-making. The output is more than a statistical result — it’s a credible, evidence-based story about what works, why it works, and how it can be improved.