Stanford University

To develop, test, and post new algorithms for estimating heterogeneous causal effects from large-scale observational studies and field experiments

  • Amount $480,854
  • City Stanford, CA
  • Investigator Susan Athey
  • Year 2017
  • Program Economics
  • Sub-program Economic Institutions, Behavior, & Performance

This work funds methodological work by economist Susan Athey, who is aiming to develop rigorous new statistical algorithms that will allow machine learning programs to isolate causal relationships in large, complex datasets. Athey is building special new tools to handle methodological tasks that economists care about but often find challenging. These include novel techniques for taking heterogeneity into account while estimating treatment effects, calculating optimal policies, and testing hypotheses in very large and varied populations. Athey’s focus will be on computing algorithms that are particularly useful for evaluating policy interventions and that enable one to isolate how policy changes differentially affect the behavior of heterogeneous populations. As a result of her work, she expects to publish several pieces in peer reviewed statistical and econometric journals and all the algorithms, code, documentation, and nonproprietary data Athey and her team generates will be made freely available to other researchers.

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