- Viviano, Davide. Policy choice in experiments with unknown interference (2021, Job Market Paper)
- Presented at Young Economist Symposium 2021 (Princeton, online), EEA-ESEM Conference, NYU Quantitative Methods Workshop
- Viviano, Davide. Policy Targeting under Network Interference, submitted (2021, Writing Sample 2)
- Presented at EEA-ESEM Conference, 2021 North American Econometric Society Summer Meeting, 6th Annual Conference of Network Science and Economics, ASSA 2021 (online), Young Economists Symposium (UPenn, online), Econometric Society World Congress 2020, Causal Learning with Interactions (UCL), EGSR Conference 2019 (Un. of Washington in St. Louis)
- Viviano, Davide, Kaspar Wüthrich and Paul Niehaus. (When) should you adjust inferences for multiple hypothesis testing? (July, 2021)
- Viviano, Davide and Jelena Bradic. Dynamic covariate balancing: estimating treatment effects over time, submitted (June, 2021)
- Presented at Causal Inference Symposium (Pardee RAND)
- Viviano, Davide. Experimental Design under Network Interference (Oct, 2021)
Draft | [arXiv]
I propose experimental designs for inference on and to estimate welfare-maximizing treatment rules under network interference.
Draft | Slides | [arXiv] | Replication code
I construct and study targeted treatments from an existing experiment or observational study in the presence of spillovers.
[arXiv] (alphabetical first author ordering)
We propose an economic framework to explain when, how, and why multiple hypothesis testing adjustments should be done differently.
[arXiv] | Slides | Replication code | Software
We study inference on dynamic treatments in the presence of high-dimensional covariates and unknown propensity scores.
Draft | [arXiv]
I propose a statistical framework for two-stage experimental designs under interference, with a focus on precise inference on treatment and spillover effects.
Revise and Resubmit
- Viviano, Davide and Jelena Bradic. Fair Policy Targeting , Revision request at the Journal of the American Statistical Association (2nd round) (May, 2021)
- Presented at EGSR Conference 2020 (Un. of Washington in St. Louis)
- Viviano, Davide and Jelena Bradic. Synthetic Learner: Model-free Inference on Treatments over Time
Revise and Resubmit at the Journal of Econometrics (September 2021)
Draft | Supplement | [arXiv] | Replication code
We study estimation and inference with machine-learning estimators in the context of synthetic controls.
[arXiv] | Replication code
We propose and study treatment allocation rules which are fair and Pareto optimal for applications in welfare programs.