Working Papers
- Jagadeesan, Ravi and Davide Viviano. Publication design with incentives in mind (September, 2024)
- Viviano, Davide and Jess Rudder. Policy design in experiments with unknown interference (May, 2024), submitted
- Imbens, Guido, Davide Viviano. Identification and Inference for Synthetic Controls with Confounding (December, 2023)
- Viviano, Davide, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi. Causal Clustering: design of cluster experiments under network interference (January, 2024)
- Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time with potential local projections (May, 2024), submitted
- Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. A model of multiple hypothesis testing (April, 2024), Revision requested at Review of Economic Studies
- Viviano, Davide. Experimental Design under Network Interference (July, 2022)
Draft
We study the design of scientific communication in settings where researchers have private incentives.
Draft | Slides | [arXiv] | Video | Field implementation (AEA Registry)
We propose an experimental design for inference on and estimation of policies under network interference.
(new: follow-up data collection in an implementation reaching over 400,000 participants.)
[arXiv]
We formalize the properties of Synthetic Control estimators with unobserved confounders.
Draft | [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).
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) dynamic selection into treatment.
[arXiv]
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.)
[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, Forthcoming at Review of Economic Studies (2024)
- 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 | [arXiv] | Supplement | Replication code
We study estimation and inference with machine-learning estimators in the context of synthetic controls.
Accepted version | Supplement | Slides | Replication code | Notes | [arXiv]
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 | [arXiv] | Supplement | Replication code
We propose and study treatment allocation rules that are fair and Pareto optimal for applications in welfare programs.