Working Papers

• Viviano, Davide. Policy design in experiments with unknown interference (March, 2023, Job Market Paper )
  Draft | Supplement | [arXiv] | Video
  I propose an experimental design for inference on and estimation of policies under network interference.
  Field implementation (AEA Registry) : Bundling Weather Forecasts and Agronomic Advisory for Farmers in Pakistan (with J. Rudder and Precision Development)
  
• Viviano, Davide. Policy Targeting under Network Interference, Reject and resubmit at Review of Economic Studies (January, 2023)
  Draft | Supplement | Slides | Replication code | Notes | [arXiv]
  I design and study targeted treatments using data from existing experiments or observational studies in the presence of spillovers.
  
• Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. (When) should you adjust inferences for multiple hypothesis testing? (August, 2022), submitted
  [arXiv] (alphabetical first author ordering)
  We propose an economic framework to explain when, how, and why multiple hypothesis testing adjustments should be done differently.
  (See also related comment on FDA advisory for handling multiple endpoints in clinical trials.)
  
• Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time (July, 2022)
  Draft | [arXiv] | Slides | Replication code | R-package
  We study inference on time-varying treatments with panel data in the presence of high-dimensional covariates and unknown propensity scores.
  
• Viviano, Davide. Experimental Design under Network Interference (July, 2022)
  [arXiv] | Replication code
  I propose a statistical framework for two-stage experimental designs under interference, with a focus on precise inference on treatment and spillover effects.

Accepted or Published Papers

• Viviano, Davide and Jelena Bradic. Fair Policy Targeting , Forthcoming in the Journal of the American Statistical Association (2022+)
  Accepted version | [arXiv] | Replication code
  We propose and study treatment allocation rules which are fair and Pareto optimal for applications in welfare programs.
  
• Viviano, Davide and Jelena Bradic. Synthetic Learner: Model-free Inference on Treatments over Time Forthcoming in the Journal of Econometrics (2022+)
  Accepted version | Supplement | [arXiv] | Replication code
  We study estimation and inference with machine-learning estimators in the context of synthetic controls.