My research studies how to design measures, experiments and communication practices to produce scientific evidence and policy choice. I acknowledge support by the NSF.

Working papers

• Breza, Emily, Arun Chandrasekhar and Davide Viviano. Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery (July, 2025), Revision requested at Econometrica
  [arXiv] | Slides
  We provide a framework and method to learn when and how effects generalize across different contexts, and when instead they do not.
• Epanomeritakis, Aristotelis and Davide Viviano. Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence (December, 2025)
  [arXiv] | Slides
  We provide an economic framework to choose which experiment to run and how to design it to estimate general equilibrium effects or externally valid counterfactuals.
• Rambachan, Ashesh, Rahul Singh and Davide Viviano. Program Evaluation with Remotely Sensed Outcomes (October, 2025)
  [arXiv] | Slides | Webinar presentation| R package | Replication code
  We provide an econometric framework for causal inference when researchers do not have direct access to the outcome, but only observe remote sensed outcomes such as satellite images or digital traces.
• Jagadeesan, Ravi and Davide Viviano. Publication design with incentives in mind (September, 2025)
  Draft | Slides | [arXiv]
  We study the design of scientific communication in settings where researchers have private incentives.
• Viviano, Davide, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi. Causal Clustering: design of cluster experiments under network interference (January, 2025)
  [arXiv] | Slides
  We study when and how researchers should design clusters in experiments with network spillovers (with an application to the universe of Facebook users).
• Viviano, Davide. Experimental Design under Network Interference (November, 2025), R&R at Journal of Political Economy: Microeconomics
  [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.
• Viviano, Davide and Jess Rudder. Policy design in experiments with unknown interference (March, 2025), R&R at Review of Economics and Statistics
  Draft | Supplement | Slides | [arXiv] | Webinar presentation | Field implementation (AEA Registry)
  We propose an experimental design for inference on and estimation of policies under network interference.
  (with follow-up data collection in an implementation reaching over 400 000 participants.)
• Imbens, Guido, Davide Viviano. Identification and Inference for Synthetic Controls with Confounding (December, 2023)
  [arXiv]
  We formalize the properties of Synthetic Control estimators with unobserved confounders.

Publications

Experiments, evidence aggregation, and policy design

• Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. A model of multiple hypothesis testing (February, 2026), Accepted at Review of Economic Studies
  Accepted version | Webinar presentation
  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. Policy Targeting under Network Interference, Review of Economic Studies (2025, featured article)
  Published version | Accepted working paper [arXiv] | Supplement | Slides | Replication code | Notes
  I design and study how to choose whom to treat in the presence of spillovers using data from existing experiments or observational studies.
  R package (developed with Jake Carlson)
• Viviano, Davide and Jelena Bradic. Fair Policy Targeting. Journal of the American Statistical Association (2024)
  Published version | Accepted working paper [arXiv] | Supplement | Replication code
  We propose and study treatment allocation rules that are fair and Pareto optimal for applications in welfare programs.

Observational methods for policy evaluation

• Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time with potential local projections, Accepted at Biometrika
  Published version | Accepted working paper [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) dynamic selection into treatment.
• Athey, Susan, Imbens Guido, Zhaonan Qu, Davide Viviano. Triple Robust Panel Estimator forthcoming at Journal of Applied Econometrics
  Accepted version
  We introduce a panel data estimator with strong robustness properties. We illustrate its properties across several applications.
• Viviano, Davide and Jelena Bradic. Synthetic Learner: Model-free Inference on Treatments over Time. Journal of Econometrics (2023)
  Published version | [arXiv] | Supplement | Replication code
  We study estimation and inference with machine-learning estimators in the context of synthetic controls.