Programme 2022
Programme of the Causal Data Science Meeting 2022, Nov 7–8.
All times are Central European Time. Use the worldtimebuddy to easily convert to your local time.
Affiliations correspond to the respective presenting author. Note on work-in-progress: Co-authors will be added.
Day 1 – November 07, 2022
| Time | Presentation |
|---|---|
| 10:30 | Welcome Paul Hünermund, Jermain Kaminski, Carla Schmitt, Beyers Louw Copenhagen Business School & Maastricht University, DK & NL |
| 10:40 | Session 1 |
| Bounding counterfactuals under selection bias Alessandro Antonucci IDSIA, Lugano, CH | |
| A proposed theoretical framework for retinal biomarkers Ian MacCormick Centre for Inflammation Research, University of Edinburgh, The Queen’s Medical Research Institute, UK | |
| Quantitative probing: Validating causal models with quantitative domain knowledge Daniel Grünbaum OSRAM Group & University of Regensburg, DE | |
| Leveraging causal relations to provide counterfactual explanations and feasible recommendations to end users Riccardo Crupi Intesa Sanpaolo, IT | |
| 11:50 | 70 min break (click for timer) |
| 13:00 | Session 2 |
| The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions Giusi Moffa Department of Mathematics and Computer Science, University of Basel, CH & Division of Psychiatry, University College London, London, UK | |
| The importance of hyperparameter tuning in causal effect estimation Damian Machlanski Department of Computer Science and Electronic Engineering, University of Essex, UK | |
| Testing the identification of causal effects in observational data Jannis Kueck University of Hamburg, Faculty of Business Administration, DE | |
| Explainable Bayesian networks applied to transport vulnerability Alta de Waal Department of Statistics, University of Pretoria, SA & Centre for Artificial Intelligence Research (CAIR), SA | |
| 14:20 | 30 min break (click for timer) |
| 14:50 | Session 3 |
| Benchpress: A scalable and versatile workflow for benchmarking structure learning algorithms Jack Kuipers ETH Zurich, CH | |
| Differentiable causal discovery under latent interventions Goncalo Faria Instituto Superior Tecnico & LUMLIS (Lisbon ELLIS Unit), Universidade de Lisboa, PT | |
| Learning Bayesian networks through Birkhoff polytope: A relaxation method Aramayis Dallakyan StataCorp, US | |
| Image-based treatment effect heterogeneity Connor Jerzak University of Texas at Austin, Department of Government, US | |
| 16:10 | 30 min break (click for timer) |
| 16:40 | Session 4 |
| Applying causal AI to industrial use cases Stuart Frost Geminos, US | |
| A dynamic bayesian model for causal inference with mediation Ho Kim University of Missouri-St. Louis, US | |
| Orthogonal policy learning under ambiguity Riccardo d’Adamo University College London, Department of Economics, UK | |
| An open-source suite of causal AI tools and libraries Emre Kiciman Microsoft Research, US | |
| 18:00 | 30 min break (click for timer) |
| 18:30 | Keynote Judea Pearl, UCLA |
| 20:00 | 30 min break (click for timer) |
| 20:30 | Causal science in the industry: A roundtable with industry leaders |
| Moderator Victor Zitian Chen Director of Experimental Design and Causal Inference, Fidelity Investments | |
| Panelists | |
| Sathya Anand Director of Data Science and Engineering, Netflix | |
| Somit Gupta Principal Data Scientist at Experimentation Platform, Microsoft | |
| Mikael Konutgan Software Engineering Manager at Experimentation Platform, Meta | |
| Benjamin Skrainka Data Science Manager in Experimentation, eBay | |
| Eric Weber Senior Director of Data Science, Experimentation, Causal Inference & Platform, Stitch Fix | |
| YinYin Yu Applied Research Manager, Experimentation & Causal Inference, LinkedIn | |
| 22:00 | End of day |
Day 2 – November 08, 2022
| Time | Presentation |
|---|---|
| 10:30 | Welcome Paul Hünermund, Jermain Kaminski, Carla Schmitt, Beyers Louw Copenhagen Business School & Maastricht University |
| 10:40 | Session 1 |
| Effect or treatment heterogeneity? Policy evaluation with aggregated and disaggregated treatments Michael Knaus University of Tübingen & IZA, Bonn, DE | |
| Can causal graphs improve estimation with Double Machine Learning? Patrick Rehill Centre for Social Research and Methods, Australian National University, AU | |
| So many choices in Double Machine Learning!? Practical insights from an extensive simulations study Oliver Schacht University of Hamburg, DE | |
| How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign Henrika Langen University of Helsinki, FI | |
| 11:50 | 70 min break (click for timer) |
| 13:00 | Session 2 |
| A field experiment on attracting crowdfunders Lars Hornuf University of Bremen, Faculty of Business Studies and Economics, DE | |
| Too casual causality: On the risks of comparing the ITCV to casual benchmarks in management research Sirio Lonati NEOMA Business School, FR | |
| Fair policy learning from observational data Dennis Frauen Institute for AI in Management, LMU Munich, DE | |
| Sophisticated consumers with inertia: Long-term implications from a large-scale field experiment Klaus Miller HEC Paris, FR | |
| 14:20 | 30 min break (click for timer) |
| 14:50 | Session 3 |
| Political networking: Consequences for cross-border acquisitions of peer firms Zhiyan Wu Erasmus University, NL | |
| Differences: A package for difference-in-differences with Python Bernardo Dionisi Fuqua School of Business, Duke University, US | |
| Pricing algorithms, nursing homes, and Covid Ben Tengelsen IntelyCare, US | |
| Structural causal modeling of managerial interventions: What if managers had not intervened by doing this? Gwendolyn Lee University of Florida, Warrington College of Business, US | |
| 16:10 | 30 min break (click for timer) |
| 16:40 | Session 4 |
| Targeted learning in observational studies with multi-level treatments: an evaluation of antipsychotic drug treatment safety for patients with serious mental illnesses Jason Poulos Harvard Medical School, Department of Health Care Policy, US | |
| Long story short: Omitted variable bias in causal machine learning Carlos Cinelli University of Washington, Department of Statistics, US | |
| Ensure a/b test quality at scale with automated randomization validation and sample ratio mismatch detection Zhang Zezhong eBay, US | |
| Exploiting selection bias on underspecified tasks in large language models Emily McMilin Independent Researcher, US | |
| 18:00 | 30 min break (click for timer) |
| 18:30 | Keynote Silvia Chiappa, DeepMind & UCL |
| 19:30 | End of day |