Across Nigeria and other emerging economies, one of the most persistent challenges for decision-makers is separating correlation from causation. Reports and dashboards often describe what is happening but leave unanswered the more critical question—why it is happening. In his book, Causal Inference in the Wild: Unlocking Cause and Effect in Observational Data Studies, data scientist Austine Unuriode presents a practical framework for bridging this gap.
The book arrives at a time when governments, enterprises, and development partners are investing heavily in digital transformation, yet much of their data remains underutilized. Observational datasets dominate public systems, financial records, health registries, and agricultural reports.
While these datasets capture valuable information, they rarely provide clarity on cause-and-effect relationships. The result is decisions made on incomplete evidence, often leading to wasted resources or ineffective policies. His work tackles this shortfall head-on, showing how institutions can strengthen their decision-making capacity without depending on experimental conditions that are often unfeasible.
Structured around practical applications, Causal Inference in the Wild lays out how causal reasoning can be embedded in systems that directly affect national development. In healthcare, it outlines strategies for evaluating whether interventions truly reduce mortality rates or simply overlap with other trends.
In agriculture, it examines how subsidies can be properly assessed for their actual impact on productivity. In financial services, it explores how reforms can be evaluated for their effect on access to credit and market stability. By rooting each example in real-world sectors, the book ensures that technical rigor is always tied to societal relevance.
One of the most notable contributions of the text is its insistence on feasibility. Rather than proposing abstract models detached from context, he emphasizes frameworks that can be scaled across institutions with varying levels of technical maturity. He highlights pathways for integrating causal methods into everyday operations, from planning ministries to enterprise data teams, ensuring that insights are not only robust but also practical to implement.
The book has generated interest across academia, policy circles, and industry forums. Universities have incorporated it into advanced courses on applied statistics and governance, while public agencies are beginning to review its frameworks for program evaluation and policy design. Regional dialogues on digital transformation and evidence-based planning have also referenced the text as a resource for strengthening decision-making capacity in West Africa.
Beyond its immediate technical contributions, Causal Inference in the Wild reflects a larger shift in how data is perceived in Nigeria’s development journey. It signals the importance of moving beyond descriptive analytics toward approaches that capture causality—methods that can underpin national planning, improve accountability, and build trust in public institutions.
In presenting this framework, he underscores that the future of data science in emerging economies lies not in volume but in validity. By unlocking the power of cause and effect in observational data, he offers a roadmap for institutions determined to make smarter, more reliable decisions in complex and rapidly changing environments.
