I'm an applied scientist at Amazon Lab126, where I use machine learning-based causal inference and prediction methods to solve problems related to Amazon devices. Recently I've worked on sleep-related devices such as the Amazon Halo Rise.

Prior to this position, I was a postdoc at the Golub Capital Social Impact Lab, led by Susan Athey at Stanford GSB. My research focused on the design and analysis of adaptive experiments such as multi-armed bandits and contextual bandits. I was a developer on the R package grf for nonparametric causal inference based on random forests.

I hold a PhD in Economics from Boston College. My advisors were Stefan Hoderlein, Arthur Lewbel and Utku Ünver.

Research

Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning (2022).
Susan Athey, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams [alphabetically].
Preprint.
[arXiv] [github]

Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives? (2021)
Susan Athey, Katy Bergstrom, Vitor Hadad, Julian C. Jamison, Berk Özler, Luca Parisotto, Julius Dohbit Sama [alphabetically].
Preprint.
[preprint]

Confidence Intervals for Policy Evaluation in Adaptive Experiments (2021).
Vitor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager and Susan Athey.
Proceedings of the National Academy of Sciences.
[arXiv] [github]

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits (2021).
Ruohan Zhan, David A. Hirshberg, Vitor Hadad and Susan Athey.
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.
[arXiv] [github]

Extending Tractable Contextual Bandits Beyond Realizability (2021).
Sanath Kumar Krishnamurthy, Vitor Hadad and Susan Athey.
International Conference on Machine Learning (ICML).
[arXiv]

Tractable Contextual Bandits Beyond Realizability (AISTATS Poster, 2021).
Sanath Kumar Krishnamurthy, Vitor Hadad and Susan Athey.
International Conference on Artificial Intelligence and Statistics (AISTATS).
[arXiv]

Sufficient Representations for Categorical Variables (2018).
Jonathan Johannemann, Vitor Hadad, Susan Athey and Stefan Wager.
Preprint.
[arXiv] [github]

Essays in Econometrics and Dynamic Kidney Exchange (2018).
Vitor Hadad.
Doctoral dissertation.
[PDF]

Software

GRF Causal inference based on generalized random forests (R/C++ package).
Based on Wager, Tibshirani and Athey (2019, Ann. Stat.) and others.
[github] [docs]

sufrep Encoding categorical variables as continuous to ML algorithms via sufficient representations (R package).
Based on Johannemann, Hadad, Athey, Wager (2018).
[github]

fhhps Linear panel data with two endogenous random coefficients (Python package).
Based on Fox, Hadad, Hoderlein, Petrin and Sherman (In progress).
[github] [slides]

Teaching

Big Data - Boston College EC3389.
Co-instructor, with Stefan Hoderlein. Spring 2016.
[github]

Statistics - Boston College EC1151.
Instructor. Fall 2016, Spring 2017.
[github]