We advance cutting-edge data analytics by developing and applying novel methods for analyzing complex datasets. Our research enables more robust and rigorous empirical research, helping researchers and practitioners solve real-world problems, and generating insights that drive impactful decision-making.

2024: Ongoing research

Beyond common support: an iterative approach to nonseparable models with endogeneity

We are developing a novel econometric approach to address endogeneity in nonseparable models by introducing a new concept called "partially connected support." This more general support condition allows us to overcome limitations of the traditional method. Our approach is tested through simulations, demonstrating superior performance in scenarios where the common support assumption is violated but partially connected support holds.

Endogeneity in nonseparable models presents significant challenges for understanding the true effects in data analytics and econometrics, and the existing method of using control variables is often constrained by the common support assumption, which can be difficult to satisfy in practice.

This approach broadens the applicability of nonseparable models, allowing for more reliable analysis and decision-making in a wide range of contexts such as education and agricultural production.

Rehabilitating the Once-Abandoned Endogenous IV

We are developing a novel econometric approach to address one of the key challenges in the instrumental variable (IV) method: the difficulty in identifying valid instruments. Our approach provides researchers with a tool to assess the validity of IVs,  and to identify causal effects even when IVs are imperfect. This approach is especially effective in large datasets where traditional IV methods may fail.

Causal inference is challenging, particularly in fields like health, education, and agriculture where complex relationships and large datasets are common. The IV approach is one of the most popular approaches to causal inference, but is often not reliable due to the difficulty to find a valid IV in practice.

This approach has broad implications for empirical research and policy-making. By offering a more robust framework for causal inference, it allows researchers to draw more reliable and robust conclusions in complex real-world scenarios. The potential applications are vast, ranging from assessing the effects of policies in health and education to evaluating agricultural interventions or policies.