4 papers · 1 filter
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…
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…
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…
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…