People

I lead the Causal Evidence and Decisions Studio (CEADS) at Yale, where we develop machine learning–aided causal inference methods for high-stakes decision-making. Our work sits at the intersection of nonparametric and semiparametric statistics, interpretable ML, and data science—grounded in first-principles thinking and process-driven science.

What do you do with Data Science?

I use data science to design machine learning–aided causal inference methods that help answer high-stakes questions in health, policy, and other applied domains. My work focuses on creating accurate approaches—able to estimate heterogeneous treatment effects in complex, small-data settings; trustworthy—transparent enough for domain experts to interpret, validate assumptions, and ensure safety; and domain-conscious—incorporating contextual knowledge to bridge the gap between research and practice. I have developed methods for integrating experimental and observational data, generalizing trial findings to new populations, and learning optimal treatment regimes, with applications ranging from "evaluating anti-seizure treatments in acute brain injury patients" to "characterizing underrepresented groups in clinical trials". My publications appear in venues such as the Journal of the American Statistical Association, Harvard Data Science Review, Journal of Machine Learning Research, Nature Communications and The Lancet Digital Health. Going forward, I aim to expand these methods to settings with complex data structures—such as networks, longitudinal records, and multi-modal health data—to enable safer and trustworthy decision-making.

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