A parallel evolutionary algorithm for subset selection in causal inference models

Wendy K.Tam Cho, Yan Y. Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Science is concerned with identifying causal inferences. To move beyond simple observed relationships and associational inferences, researchers may employ randomized experimen-tal designs to isolate a treatment effect, which then per-mits causal inferences. When experiments are not prac-tical, a researcher is relegated to analyzing observational data. To make causal inferences from observational data, one must adjust the data so that they resemble data that might have emerged from an experiment. Traditionally, this has occurred through statistical models identified as match-ing methods. We claim that matching methods are unnecessarily constraining and propose, instead, that the goal is better achieved via a subset selection procedure that is able to identify statistically indistinguishable treatment and control groups. This reformulation to identifying optimal subsets leads to a model that is computationally complex. We develop an evolutionary algorithm that is more efficient and identifies empirically more optimal solutions than any other causal inference method. To gain greater efficiency, we also develop a scalable algorithm for a parallel computing environment by enlisting additional processors to search a greater range of the solution space and to aid other processors at particularly difficult peaks.

Original languageEnglish (US)
Title of host publicationProceedings of XSEDE 2016
Subtitle of host publicationDiversity, Big Data, and Science at Scale
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450347556
DOIs
StatePublished - Jul 17 2016
EventConference on Diversity, Big Data, and Science at Scale, XSEDE 2016 - Miami, United States
Duration: Jul 17 2016Jul 21 2016

Publication series

NameACM International Conference Proceeding Series
Volume17-21-July-2016

Other

OtherConference on Diversity, Big Data, and Science at Scale, XSEDE 2016
CountryUnited States
CityMiami
Period7/17/167/21/16

Keywords

  • Combinatorial optimization
  • Evolutionary algorithm
  • Message passing
  • Parallel computing

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A parallel evolutionary algorithm for subset selection in causal inference models'. Together they form a unique fingerprint.

  • Cite this

    Cho, W. K. T., & Liu, Y. Y. (2016). A parallel evolutionary algorithm for subset selection in causal inference models. In Proceedings of XSEDE 2016: Diversity, Big Data, and Science at Scale [a7] (ACM International Conference Proceeding Series; Vol. 17-21-July-2016). Association for Computing Machinery. https://doi.org/10.1145/2949550.2949568