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studies. Strong statistical skills in causal inference and the ability to manage various sources of bias are essential. The ideal candidate will also have experience in both aging and environmental
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large cohort studies. Strong statistical skills in causal inference and the ability to manage various sources of bias are essential. The ideal candidate will also have experience in both aging and
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. • Demonstrated expertise in conducting agricultural stakeholder surveys, processing raw survey data into a usable format using statistical software programs, and analyzing data to perform causal inference using
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work on cutting-edge empirical research related to policy evaluation, climate impacts, and market analysis, leveraging synthetic control techniques to address spatial dependencies and causal inference
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structural modeling and estimation. Advanced knowledge in causal inference and use of econometric tools and statistical software (e.g., Stata, R, Python) to conduct research using secondary and primary data