My two areas of active work are summarized below with links to selected papers. More information can be found on my Google Scholar profile page.

Causal inference in fairness

This work approaches fair machine learning from a causal inference perspective, arguing that determining what is fair is a similar challenge to determining causality.


Some of this work was covered in the New Scientist and 36Kr.


Post-selection inference

This work involves challenging mathematical and computational aspects of conducting inference after selection procedures with complicated underlying geometry. For example, this research enables significance testing after use of some of the most popular model selection procedures such as the lasso with regularization chosen by cross-validation, or forward stepwise with number of steps chosen by AIC or BIC. Often the resulting significance tests are slightly modified versions of the classical tests in regression analysis.


I’m a co-author of the selectiveInference package on CRAN.


Collaborations and other projects


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".