Research


Publications, preprints, code

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.

Press

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

Publications

  • M. J. Kusner, J. R. Loftus, C. Russell, R. Silva. Counterfactual fairness. Advances in Neural Information Processing Systems, 2017. [link]

  • C. Russell, M. J. Kusner, J. R. Loftus, R. Silva. When worlds collide: integrating different counterfactual assumptions in fairness. Advances in Neural Information Processing Systems, 2017. [link]

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.

Software

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

Publications

  • J. E. Taylor, J. R. Loftus, and R. J. Tibshirani. Inference in adaptive regression via the Kac-Rice formula. Annals of Statistics, 2016. [link]

  • J. R. Loftus. Selective inference after cross-validation. Preprint.

  • J. R. Loftus and J. E. Taylor. Selective inference in regression models with groups of variables. Preprint.

  • J. R. Loftus, J. E. Taylor. A significance test for forward stepwise model selection. Preprint.

  • X. Tian, J. R. Loftus, J. E. Taylor. Selective inference with unknown variance via the square-root LASSO. Submitted.

Collaborations and other projects

  • Xu, J., et al. Landscape of monoallelic DNA accessibility in mouse embryonic stem cells and neural progenitor cells. Nature genetics. Chang lab. [link]

  • E. T. Richardson, et. al. Modeling cash incentives vs. oral preexposure prophylaxis in high-risk African women: the Cash-PreP Study. Poster, AIDS Conference.

  • Team leader at 2013 DataFest competition. We won a $1,000 prize for our project applying causal inference to US Senate electoral finance data.

  • Conducted extensive crowd-sourcing experiments during an internship at Google in 2013.