You find my research organized in three related areas with areas and papers listed alphabetically.

Generalizable evidence and scientific communication for program evaluation

Designing experiments, measures, and communication practices to produce scientific evidence.

• Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. A model of multiple hypothesis testing (November, 2025), Revise and resubmit Review of Economic Studies (3rd round)
  Draft | [arXiv] | 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.)
• Epanomeritakis, Aristotelis and Davide Viviano. Choosing What to Learn: Experimental Design when Combining Experimental with Observational Evidence (November, 2025)
  Draft | [arXiv] | Slides
  We study which experiment to run (and how) when experimental evidence is combined with structural or reduced-form observational estimates. We present two empirical applications studying external validity and GE effects.
• Breza, Emily, Arun Chandrasekhar and Davide Viviano. Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery (July, 2025)
  [arXiv] | Slides
  We provide a framework and method to learn when and how effects generalize across different contexts, and when instead they do not.
• 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.

Supported by NSF Grant SES 2447088

Policy design and interference

Using data to design policies and experiments with a focus on spillovers and social interactions.

• 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)
  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.)

Publications

• Viviano, Davide. Policy Targeting under Network Interference, Review of Economic Studies (2024, featured article)
  Published version | 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 (2023)
  Published version | Working paper [arXiv] | Supplement | Replication code
  We propose and study treatment allocation rules that are fair and Pareto optimal for applications in welfare programs.

Policy evaluation with observational studies

Methods for statistical inference with high-dimensional covariates, panel data, and event studies.

• Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time with potential local projections (July, 2025), Revise and resubmit at Biometrika
  Draft | Supplement | Longer version | [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.
• 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.
• Athey, Susan, Imbens Guido, Zhaonan Qu, Davide Viviano. Triple Robust Panel Estimator (September, 2025), conditionally accepted at Journal of Applied Econometrics
  [arXiv]
  We introduce a panel data estimator with strong robustness properties. We illustrate its properties across several applications.

Publications

• 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.