3 citations · 7 across the 5 of their papers we have counts for
5 papers
Causal inference and policy evaluation without a control group
Augusto Cerqua, Marco Letta, Fiammetta Menchetti
Without a control group, the most widespread methodologies for estimating causal effects cannot be applied. To fill this gap, we propose the Machine Learning Control Method, a new…
Causal effect of regulated Bitcoin futures on volatility and volume
Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
In December 2017, two leading derivative exchanges, CBOE and CME, introduced the first regulated Bitcoin futures. Our aim is estimating their causal impact on Bitcoin volatility an…
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach
Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have be…
Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models
Fiammetta Menchetti, Iavor Bojinov
The Florence branch of an Italian supermarket chain recently implemented a strategy that permanently lowered the price of numerous store brands in several product categories. To qu…
Evaluating Federal Policies Using Bayesian Time Series Models: Estimating the Causal Impact of the Hospital Readmissions Reduction Program
Georgia Papadogeorgou, Fiammetta Menchetti, Christine Choirat +3
Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and…