Preprints
- Breza, Emily, Arun Chandrasekhar and Davide Viviano. Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery (January, 2025)
- Rambachan, Ashesh, Rahul Singh and Davide Viviano. Program Evaluation with Remotely Sensed Outcomes (April, 2025)
- Jagadeesan, Ravi and Davide Viviano. Publication design with incentives in mind (April, 2025)
- Viviano, Davide and Jess Rudder. Policy design in experiments with unknown interference (March, 2025)
- Viviano, Davide, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi. Causal Clustering: design of cluster experiments under network interference (January, 2025)
- Imbens, Guido, Davide Viviano. Identification and Inference for Synthetic Controls with Confounding (December, 2023)
[arXiv] | Slides
We provide a framework and method to learn when and how effects generalize across different contexts, and when instead they do not.
[arXiv] | Slides | Webinar presentation
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.
[arXiv] | Slides
We study the design of scientific communication in settings where researchers have private incentives.
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.)
[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).
[arXiv]
We formalize the properties of Synthetic Control estimators with unobserved confounders.
Under revision
- Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. A model of multiple hypothesis testing (January, 2025), Revision request at Review of Economic Studies (2nd round)
- Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time with potential local projections (December, 2024), Revision requested at Biometrika
- Viviano, Davide. Experimental Design under Network Interference (July, 2022), Revision requested at Journal of Political Economy:Microeconomics
[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.)
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.
[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. Policy Targeting under Network Interference, Review of Economic Studies (2024, featured article)
- Viviano, Davide and Jelena Bradic. Fair Policy Targeting. Journal of the American Statistical Association (2023)
- Viviano, Davide and Jelena Bradic. Synthetic Learner: Model‑free Inference on Treatments over Time. Journal of Econometrics (2023)
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)
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.
Published version | [arXiv] | Supplement | Replication code
We study estimation and inference with machine‑learning estimators in the context of synthetic controls.