activity
20202024
most citedAnalysing a built-in advantage in asymmetric darts contests using causal machine learning

9 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

econ.GN2024★ 1 cited

Tournaments, Contestant Heterogeneity and Performance

Enzo Brox, Daniel Goller

Tournaments are frequently used incentive mechanisms to enhance performance. In this paper, we use field data and show that skill disparities among contestants asymmetrically affec…

econ.GN2023

'Good job!' The impact of positive and negative feedback on performance

Daniel Goller, Maximilian Späth

We analyze the causal impact of positive and negative feedback on professional performance. We exploit a unique data source in which quasi-random, naturally occurring variations wi…

stat.AP2022★ 1 cited

A general framework to quantify the event importance in multi-event contests

Daniel Goller, Sandro Heiniger

We propose a statistical framework for quantifying the importance of single events that do not provide intermediate rewards but offer implicit incentives through the reward structu…

econ.GN2021

Active labour market policies for the long-term unemployed: New evidence from causal machine learning

Daniel Goller, Tamara Harrer, Michael Lechner +1

Active labor market programs are important instruments used by European employment agencies to help the unemployed find work. Investigating large administrative data on German long…

econ.EM2020★ 9 cited

Analysing a built-in advantage in asymmetric darts contests using causal machine learning

Daniel Goller

We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we a…