November 16, 2021

Every conference day consists of three presentation blocks, followed by a keynote talk in the evening. Times are Central European Time (CET). Registered participants will receive a link to Zoom to join the Meeting.

Paul Hünermund, Jermain Kaminski, Carla Schmitt, Beyers Louw
Copenhagen Business School / Maastricht University
Session 1
10:40The impact of the #MeToo movement on language at court: A text-based causal inference approach
Henrika Langen
University of Fribourg
10:55Firm incentives and consumer adaption in a multi unit auction
Simon Schulten
Düsseldorf Institute for Competition Economics (DICE)
11:10Drawing (causal) conclusions from data – some evidence
Karsten Lübke
FOM University of Applied Sciences
11:25End-to-end causal analysis in Python with cause2e
Daniel Gruenbaum
11:40Q & A
12:0060 min break (Timer)
Session 2
13:00The role of the propensity score in fixed effect models
Dmitry Arkhangelsky
Center for Monetary and Financial Studies (CEMFI)
13:15The challenges of measuring the impact of interventions in brick-and-mortar stores
Patrick de Oude
Albert Heijn
13:30Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs
Giusi Moffa
University of Basel
13:45Causal inference with proxy variables in
Christina Katsimerou
14:00Q & A
14:2030 min break (Timer)
Session 3
14:50Treatment effects in strategic management: with an application to choosing early stage venture capital
Jorge Guzman
Columbia Business School
15:05Deep learning for individual heterogeneity: An automatic inference framework
Max Farrell
The University of Chicago Booth School of Business
15:20Causal knowledge graph: A demonstration
Victor Chen
University of North Carolina at Charlotte
15:35An empirical analysis of intra-firm product substitutability in fashion retailing
Nathan Yang
Cornell University
15:50Q & A
16:1030 min break (Timer)
Session 4
16:40The importance of being causal
Iavor Bojinov
Harvard Business School
16:55Desiderata for representation learning: A causal perspective
Yixin Wang
University of Michigan
17:10What experimental designs justify two-way-fixed-effects regression estimators
Lihua Lei
Stanford University
17:25CausalML: A Python package for uplift modeling and causal inference with machine learning
Zhenyu Zhao, Totte Harinen
Tencent, Toyota Research
17:40Q & A
18:0030 min break (Timer)
A Design Approach to Synthetic Controls
Guido Imbens
Stanford University