Randomization Tests and Causal Inference

Randomization tests for peer effects in group formation experiments — slides
with Basse, G., Ding, P., Feller, A. (2024), Econometrica, 92(2), pp. 567–590.

Minimax designs for causal effects in temporal experiments with treatment habituation
with Basse, G., Ding, Yi (2023). Biometrika, 110(1), pp. 155-168.

A graph-theoretic approach to randomization tests of causal effects under interference — arxiv code slides | CBR Article
with Puelz, D., Basse, G., Feller, A. (2022). Journal of the Royal Statistical Society, Series B, 84(1), pp.174-204.

Randomization tests in observational studies with staggered adoption of treatment — code slides
with Shaikh, A. (2021). Journal of the American Statistical Association, 116(536), pp. 1835-1848.

Robust inference for high-dimensional linear models via residual randomization
with Wang, S., Lee S.K., Kolar, M. (2021). International Conference on Machine Learning (ICML), oral.

Estimation of Covid-19 prevalence from serology tests: A partial identification approach — code slides | CBR Article (most read in 2020)
(2020) Journal of Econometrics, 220(1), pp. 193-213.

Randomization tests of causal effects under interference — supplement slides
with Basse, G., Feller, A. (2019). Biometrika, 106(2), pp. 487-494.

Long-term causal effects via behavioral game theory — slides
with Parkes, DC. (2016). Neural Information and Processing Systems (NIPS).

Estimation of causal peer influence effects
with Kao, E. (2013). International Conference on Machine Learning (ICML), oral.

Statistical Machine Learning

“Plus/Minus the Learning Rate”: Easy and Scalable Inference with SGD - slides
with Chee, J., Kim, H. (2023). International Conference on AI and Statistics (AISTATS’23).

The proximal Robbins-Monro method — supplement code slides
with Horel, T., Airoldi, EM. (2021). Journal of the Royal Statistical Society, Series B, 83(1), pp. 188-212.

Dynamical systems theory for causal inference with application to synthetic controls — slides
with Ding, Yi (2020). International Conference on AI and Statistics (AISTATS’20).

Convergence diagnostics for stochastic gradient descent with constant step size — slides
with Chee, J. (2018). International Conference on AI and Statistics (AISTATS’18), oral.

Asymptotic and finite-sample properties of estimators based on stochastic gradients —code slides errata supplement errata
with Airoldi, EM. (2017). Annals of Statistics, 45(4), pp. 1694-1727.

Towards stability and optimality in stochastic gradient descent
with Tran, D., Airoldi, EM. (2016). International Conference on AI and Statistics (AISTATS’16), oral

Scalable estimation strategies based on stochastic approximations: classical results and new insights
with Airoldi, EM. (2015). Statistics and Computing, 25(4), pp. 781-795.

Microeconomics and Networks

Design and analysis of multi-hospital kidney-exchanges using random graphs — code slides
with Parkes, DC. (2015). Games and Economic Behavior, 91, pp. 360-382.

Incentive-compatible experimental design — slides
with Zhou, J., Pfeffer, E., Parkes, DC. (2015).  Economics and Computation (EC’15).

A Random Graph Model of Kidney Exchanges: Efficiency, Individual Rationality, Incentives
with Parkes, DC. (2011). Economics and Computation (EC'11).

Online social networking, Face Recognition, and Interactive Robotics
with Mavridis, N., Kazmi, W., Ben-AbdelKader, C.(2009). CASoN'09.

Book chapters

Stochastic gradient methods for estimation with large datasets
with Airoldi, EM. (2016). Handbook of Big Data, CRC Press, eds. Buhlmann et. al.

Friends with Faces: How Social Networks Can Enhance Face Recognition and Vice Versa
with Mavridis, N., Kazmi, W. (2010). Computational Social Network Analysis, Springer London, eds. A. Ajith et. al.