1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Group-Level Treatment Effect Heterogeneity in Difference-in-Differences: A Balanced Approach
Nora Bearth, Nadja van 't Hoff, Torben S. D. Johansen
Understanding how treatment effects vary across groups is central to policy evaluation. In Difference-in-Differences designs, heterogeneity is often studied using subgroup or tripl…
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…
Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
Daniele Ballinari, Nora Bearth
In the last decade, machine learning techniques have gained popularity for estimating causal effects. One machine learning approach that can be used for estimating an average treat…
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…