An Optimal Design Framework for Lasso Sign Recovery

TL;DR

This paper develops a new framework for designing supersaturated experiments that optimizes the likelihood of correctly identifying active factors using the lasso, addressing key open problems in design optimality and sign recovery.

Contribution

It introduces criteria for maximizing lasso sign recovery probability and proposes an efficient algorithm for selecting optimal supersaturated designs.

Findings

Orthogonal designs are optimal when factor signs are unknown.

Designs with small positive correlations are optimal when signs are known.

The proposed algorithm effectively identifies designs with high sign recovery probability.

Abstract

Supersaturated designs investigate more factors than there are runs, and are often constructed under a criterion measuring a design's proximity to an unattainable orthogonal design. The most popular analysis identifies active factors by inspecting the solution path of a penalized estimator, such as the lasso. Recent criteria encouraging positive correlations between factors have been shown to produce designs with more definitive solution paths so long as the active factors have positive effects. Two open problems affecting the understanding and practicality of supersaturated designs are: (1) do optimal designs under existing criteria maximize support recovery probability across an estimator's solution path, and (2) why do designs with positively correlated columns produce more definitive solution paths when the active factors have positive sign effects? To answer these questions, we develop criteria maximizing the lasso's sign recovery probability. We prove that an orthogonal design is an ideal structure when the signs of the active factors are unknown, and a design constant small, positive correlations is ideal when the signs are assumed known. A computationally-efficient design search algorithm is proposed that first filters through optimal designs under new heuristic criteria to select the one that maximizes the lasso sign recovery probability.