Current navigation level
Previous navigation level
Next navigation level
Current navigation level

Economic Policy and Causal Machine Learning

The goal is to apply recent methodological advances in the field of causal machine learning to improve policy making.

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) and the project Chances and risks of data-driven decision making for labour market policy is part of the National Research Programme 77 “Digital Transformation” (NRP 77).

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
2020-05 Roland Hodler, Michael Lechner, Paul Raschky Reassessing the Resource Curse Using Causal Machine Learning DP-2005
2020-04 Michael Knaus Double Machine Learning Based Program Evaluation under Unconfoundedness DP-2004
2020-03 Audrino, F.; Chassot, J.; Knaus, M.; Lechner, M.; Ortega, J.-P. How does Post-Earnings Announcements Sentiment Affect Firms' Dynamicy? New Evidence from Causal Machine Learning DP-2003
2020-02 Hugo BodoryMartin HuberLukáš Lafférs
 Evaluating (weighted) Dynamic Treatment Effects by Double Machine Learning DP-2002
2020-01 Daniel Goller Analysing a Built-In Advantage in Asymmetric Darts Contests Using Causal Machine Learning DP-2001
2019-3 Bart Cockx, Michael Lechner, Joost Bollens Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
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
Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed
2018-06 Anthony Strittmatter
What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation
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
Michael Knaus A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills DP-1802
Michael Zimmert  The Finite Sample Performance of Treatment Effects Estimators based on the Lasso
2017-01 Michael Knaus, Michael Lechner, Anthony Strittmatter
Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach