Keynote I on "Causal Machine Learning" by Michael Lechner
Speaker: Michael Lechner (St. Gallen University )
Moderation: Melanie Schienle (KIT Karlsruhe)
In recent years microeconometrics experienced the ‘credibility revolution’ culminating in the 2021 Nobel prices for David Card, Josh Angrist and Guido Imbens. These developments, hopefully, lead to more reliable estimation of causal effects of certain public policies. At same time, computer science, and to some extent also statistics, developed powerful algo-rithms (Machine Learning) that are very successful in prediction tasks. The new literature on Causal Machine Learning attempts to unit these two developments, i.e., use Machine Learn-ing to improve causal analysis. In this talk, I review some of these developments. Subsequent-ly, I use an empirical example from the field of active labour market evaluation to show how these methods can be fruitfully applied to improve the usefulness of empirical studies w.r.t. to evaluating and improving policies. I conclude with some considerations about current short-comings and possible future developments of these methods.
A broadcast will take place in room HS 118 and HS 120.