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Justin Young

I am a PhD candidate in the Economics department, broadly interested in causal inference and machine learning. My research centers around developing tools to help researchers elicit causal effects under confounding, primarily in panel settings where units are tracked over time. I am also interested in understanding how econometric methods fare in practice on (tech) industry data, as datasets in academia often exhibit a degree of smoothness not present in industry. Prior to Stanford, I completed my undergrad at Yale where I spent my time doing math and music. Outside of work, I love playing the keys and producing R&B.