In today’s world, data remains our most powerful tool for unraveling unanswered questions. With technological advancements and accessibility, researchers now find it easier than ever to collect new data or access existing datasets. However, this process is far from straightforward. Empirical research demands meticulous attention to detail, as it involves two critical stages: first, ensuring data is accurately gathered, and second, analyzing it correctly to derive meaningful insights.

While data collection and analysis are inherently linked, they are often treated separately in research. Typically, researchers either construct experiments with causal inferences naturally arising from the robustness of the experimental design or extract causal relationships from imperfect data with the help of econometric techniques. Both approaches are crucial, yet integrating these steps is key to robust and reliable research outcomes.

My approach combines experimental design and econometric analysis to achieve robust causal inference while closely reflecting participants’ daily reality. I design experiments that simulate scenarios they might encounter outside the survey environment, utilizing reality-based visuals, designs, and AI. I also apply econometric techniques to extrapolate findings from experimental data to broader populations and to address situations where direct data collection is either not feasible or necessary.