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 Bodory, Martin Huber, Lukáš 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	DP-1903
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	DP-1901
2018-06	Anthony Strittmatter	What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation	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	Michael Knaus	A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills	DP-1802
2018-01	Michael Zimmert	The Finite Sample Performance of Treatment Effects Estimators based on the Lasso	DP-1801
2017-01	Michael Knaus, Michael Lechner, Anthony Strittmatter	Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach	DP-1701