[gov3009-l] Applied Stats next Wednesday (4/30): Nathan Kallus

Wise, Tess wise at fas.harvard.edu
Fri Apr 25 14:14:39 EDT 2014

Hi Everyone!

It's hard to believe that next week will be our final meeting for the semester. I have thoroughly enjoyed getting to spend my Wednesday lunches with all of you. Our final speaker will be Nathan Kallus who is a PhD student in Operations Research at MIT. Nathan will be presenting some very exciting research on Regression-Robust Designs of Controlled Experiments. The abstract and a link to the paper is included below.

As usual, we will meet in CGIS K354 at 12 noon. There will be some sort of food -- we only have $140 left in the budget so I will have to be creative. Maybe I will cook something......no promises!


Achieving balance between experimental groups is a cornerstone of causal inference. Without balance any observed difference may be attributed to a difference other than the treatment alone. In controlled/clinical trials, where the experimenter controls the administration of treatment, complete randomization of subjects has been the golden standard for achieving this balance because it allows for unbiased and consistent estimation and inference in the absence of any a priori knowledge or measurements. However, since estimator variance under complete randomization may be slow to converge, experimental designs that balance pre-treatment measurements (baseline covariates) are in pervasive use, including randomized block designs, pairwise-matched designs, and re-randomization. We formally argue that absolutely no balance better than complete randomization's can be achieved without partial structural knowledge about the treatment effects. Therefore, that balancing designs are in popular use, are advocated, and have been proven in practice means that some structural knowledge is in fact available to the researcher. We propose a novel framework for formulating such knowledge using functional analysis. It subsumes all of the aforementioned designs in that it recovers them as optimal under different choices of structure, thus theoretically characterizing their underlying motivations and comparative power under different assumptions and providing extensions of these to multi-arm trials. Furthermore, it suggests new optimal designs that are based on more robust nonparametric modeling and that offer extensive gains in precision and power. In certain cases we are able to argue linear convergence 1/2^O(-n) to the sample average treatment effect (as compared to the usual logarithmic convergence O(1/sqrt(n))). We theoretically characterize the unbiasedness, variance, and consistency of any estimator arising from our framework; solve the design problem using modern optimization techniques; and develop appropriate inferential algorithms to test differences in treatments. We uncover connections to Bayesian experimental design and make extensions to dealing with non-compliance.

Pre-print available at:

Tess Wise
PhD Candidate
Harvard Department of Government

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