no code implementations • 9 Sep 2021 • Daniel Jacob
We propose the outcome-adaptive random forest (OARF) that only includes desirable variables for estimating the propensity score to decrease bias and variance.
no code implementations • 20 Apr 2021 • Daniel Jacob
For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin.
no code implementations • 17 Dec 2020 • Daniel Jacob, Romain David, Sophie Aubin, Yves Gibon
Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how.
no code implementations • 7 Nov 2019 • Daniel Jacob
To control for confounding in the linear model, we use Neyman-orthogonal moments to partial out the effect that covariates have on both, the treatment assignment and the outcome.
1 code implementation • 1 Oct 2019 • Johannes Haupt, Daniel Jacob, Robin M. Gubela, Stefan Lessmann
To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization.