1 citations · 4 across the 6 of their papers we have counts for
6 papers
Fairness-Aware and Interpretable Policy Learning
Nora Bearth, Michael Lechner, Jana Mareckova +1
Fairness and interpretability play an important role in the adoption of decision-making algorithms across many application domains. These requirements are intended to avoid undesir…
From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies
Federica Mascolo, Nora Bearth, Fabian Muny +2
Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in…
Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
Hugo Bodory, Federica Mascolo, Michael Lechner
Decision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to…
Comprehensive Causal Machine Learning
Michael Lechner, Jana Mareckova
Uncovering causal effects in multiple treatment setting at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to…
Teamwork and Spillover Effects in Performance Evaluations
Enzo Brox, Michael Lechner
This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high-quality data on football matches to measur…
Causal Machine Learning for Moderation Effects
Nora Bearth, Michael Lechner
It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups. The causal machine learning literature has recently provided tools…