4 papers
Double Machine Learning with High-dimensional Interactive Fixed Effects
Binzhi Chen, Annalivia Polselli, Paul S. Clarke
Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE…
Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
Anna Baiardi, Paul S. Clarke, Andrea A. Naghi +1
Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confoundin…
xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
Annalivia Polselli
The double machine learning (DML) method combines the predictive power of machine learning with statistical estimation to conduct inference about the structural parameter of intere…
Double Machine Learning for Static Panel Models with Fixed Effects
Paul S. Clarke, Annalivia Polselli
Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel doub…