“If something is important enough, even if the odds are against you, you should still do it.”

– Elon Musk

* I am a Research Scientist II at Amazon, working on Causal Inference, Deep Learning, Search, Robotics, and more.

I obtained my Ph.D. in Economics at the Stanford Graduate School of Business in 2017. I worked at the intersection of Econometrics, Statistics, and Machine Learning to seek economic insights. In my thesis, I developed methods to study causal inference in complex settings. Before joining Stanford, I obtained my undergraduate degree in Mathematics and Economics with a minor in Management Science at MIT in 2013.

I was fortunate to have Guido Imbens as my advisor as well as Han Hong and Mohsen Bayati in my Ph.D. committee.

* CAUSAL INFERENCE, and not prediction, will be the main factor in shaping the future of truly intelligent machines. The ultimate goal of any object is to make decisions. While the study of prediction might help with the decision making process, only the study of causal inference can help making that process credible. This is not to say that causal inference will stay away from the great upward movement of machine learning and particularly deep learning; the use of such advanced prediction techniques in the causal inference framework will combine the best from both worlds. This in turn will help making decisions with more accuracy and confidence.


Research (* indicates equal or alphabetical authorship)

Reach me at thaipham at stanford dot edu or
Locate me below!
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