Projects on Censored Data & Causal Inference

- Censored Data and Causality Projects:

Efficient, Double Robust Estimation in a Weight Loss Study.
(with Daniel Rubin and Nick Jewell)

Study on the Consequences of the Protease Inhibitor Era (SCOPE).
(with Steve Deeks, Jeff Martin, Art Reingold, and Maya Peterson)

Data Adaptive Causal inference for Time-Independent Treatment based on longitudinal data.
(with Ira Tager and Romain Neugebauer))

A causal inference approach for constructing transcriptional regulatory networks.
(with Biao Xing)

A unified approach to censored data & causality
(with James Robins)

Complicated censored data structures with no inefficient estimators
(with Nick Jewell)

The locally efficient one-step estimator based on extended current status data in action
(with Chris Andrews)

Estimating a survival distribution with current status data and time-dependent covariates
(with Aad van der Vaart)

Estimation of the multivariate survival function based on right-censored data
(with Chris Quale and James Robins)

Estimation with bivariate right-censored data and time-dependent covariate processes
(with Sunduz Keles and James Robins)

Locally efficient estimation in a two-sample problem
(with Scott Zeger and Francesca Dominici)

Estimation of the survival distribution based on right-censored truncated data, when death is subject to reporting delay
(with Alan Hubbard)

- Causal Inference Projects:

Analyzing dynamic regimes using structural nested mean and failure time models
(with James Robins, Susan Murphy, Alan Brookhart)

Marginal structural models in action
(with Jennifer Bryan, Zhuo Yu)

Causal inference with instrumental variables
(with Alan Hubbard, Tanya Henneman)