2 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning control variables and instruments for causal analysis in observational data
Nicolas Apfel, Julia Hatamyar, Martin Huber +1
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome…
The Generalized Falsification Adaptive Set for Violations of the Exclusion Restriction and Exogeneity
Nicolas Apfel, Frank Windmeijer
The falsification adaptive set (FAS) as proposed by Masten and Poirier (2021) provides an identified set for a treatment effect when the baseline model is falsified, assuming inval…
Detecting Grouped Local Average Treatment Effects and Selecting True Instruments
Nicolas Apfel, Helmut Farbmacher, Rebecca Groh +2
Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local avera…
Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable Estimation
Nicolas Apfel
Many economic studies use shift-share instruments to estimate causal effects. Often, all shares need to fulfil an exclusion restriction, making the identifying assumption strict. T…