The availability of “Big Data” and abundance of computing power triggered the development of new methods for causal analysis based on machine learning. Our research interest is to investigate the potential of these causal machine learning methods and to make them applicable for real world policy questions. They can guide optimal policy decisions and help to systematically evaluate existing policy measure.
This research receives funding from the Swiss National Science Foundation. The project Causal analysis with Big Data is part of the National Research Programme 75 “Big Data” (NRP 75).
The current discussion papers are published on this page. Please contact the authors directly if you have any content-related questions or suggestions.
Discussion Papers
2019-3 |
Bart Cockx, Michael Lechner, Joost Bollens |
Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
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DP-1903
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2019-2 |
Michael Zimmert, Michael Lechner |
Nonparametric estimation of causal heterogeneity under high-dimensional confounding |
DP-1902 |
2019-01 |
Daniel Goller, Michael Lechner, Andreas Moczall, Joachim Wolff
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Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed
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DP-1901 |
2018-06 |
Anthony Strittmatter
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What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation
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DP-1806 |
2018-05 |
Michael Lechner |
Modified Causal Forest for Estimating Heterogeneous Causal Effects |
DP-1805 |
2018-04 |
Michael Zimmert |
Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding |
DP-1804 |
2018-03 |
Michael Knaus, Michael Lechner, Anthony Strittmatter |
Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence |
DP-1803 |
2018-02
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Michael Knaus |
A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills |
DP-1802 |
2018-01
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Michael Zimmert |
The Finite Sample Performance of Treatment Effects Estimators based on the Lasso
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DP-1801 |
2017-01 |
Michael Knaus, Michael Lechner, Anthony Strittmatter
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Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach
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DP-1701
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