Bridging Industry and Academia
in Causal Data Science

Fostering a dialogue between industry and academia on causal data science.

Save the date
November 5–6 2024, online.

Maastricht University logoCopenhagen Business School logo
Programme01
13:00–14.20
Room Copenhagen
Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach (University of Hamburg), Victor Chernozhukov (Massachusetts Institute of Technology), Carlos Cinelli (University of Washington), Lin Jia (Booking.com), Sven Klaassen (Economic AI), Nils Skotara (Booking.com), Martin Spindler (Economic AI)
Causal Inference in Industry (Working)
Hanna Post (Henkel Adhesives Technologies)
DISCO: constrained bandits for personalized discount targeting within fashion e-commerce
Jason Shuo Zhang (ASOS.com), Benjamin Howson (Imperial College London), Panayiota Savva (ASOS.com), Eleanor Loh (ASOS.com)
Structural Causal Models in Strategic Decision-Making
Carla Schmitt (Maastricht University), Jermain Kaminski (Maastricht University), Paul Hünermund (Copenhagen Business School)
Room Maastricht
Causal Targeting for Mobile Push Notifications
Moritz von Zahn (Goethe University Frankfurt), Arda Güler (Goethe University Frankfurt), Kevin Bauer (University of Mannheim), Oliver Hinz (Goethe University Frankfurt)
Accelerating Experimentation: Bayesian Sequential A/B Testing’s Role in the Tech Industry
Richie Lee (Microsoft), Max Knobbout (Uber)
Causal Inference Tools to Assess the Impact of Marketing and Advertising Initiatives on Key Business Metrics at Bolt
Anton Bugaev (Bolt)
Bridging Causal Discovery and Media Mix Modeling
Carlos Trujillo (PyMC Labs), Benjamin Vincent (PyMC Labs)
14:50–16.10
Room Copenhagen
Causal Claims in Economics
Prashant Garg (Imperial College London), Thiemo Fetzer (University of Warwick & Bonn)
Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Media
Bob Kubinec (University of South Carolina), Aseem Mahajan (New York University)
Assessing the Heterogeneous Impact of Economy-Wide Shocks
Marco Dueñas, Federico Nutarelli, Víctor Ortiz-Giménez, Massimo Riccaboni, Francesco Serti (IMT School for Advanced Studies Lucca)
Credit Ratings: Heterogeneous Effect on Capital Structure
Helmut Wasserbacher (Novartis), Martin Spindler (Economic AI)
Room Maastricht
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot (Ekimetrics, TAU, LISN, INRIA Saclay), Alessandro Leite (TAU, LISN, INRIA Saclay), Nicolas Chesneau (Ekimetrics), Michèle Sébag (TAU, LISN, INRIA Saclay), Marc Schoenauer (TAU, LISN, INRIA Saclay)
A Probabilistic Easy Variational Approach to Causal Inference in Complex Datasets
Usef Faghih (University of Quebec), Amir Saki (University of Quebec)
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
Aramays Dallakyan (StataCorp), Yang Ni (Texas A&M University)
dagrad: A Python Library for Gradient-Based Causal DAG Learning
Bryon Aragam (University of Chicago)
16:40–18.00
Room Copenhagen
Spillover Reduction Using Cluster-Randomized Experiments: Empirical Evidence and Learnings from Online Gaming Platform Roblox
Xiaochen Zhang, Shan He, Yihua Jiang (Roblox Corporation)
Causal Neuro-Symbolic AI: A Synergy Between Causality and Neuro-Symbolic Methods
Utkarshani Jaimini (Artificial Intelligence Institute at University of South Carolina), Cory Henson (Bosch Center for Artificial Intelligence), Amit Sheth (Artificial Intelligence Institute at University of South Carolina)
Batched Adaptive Team Formation
Yan Xu (Virginia Tech), Bo Zhou (Virginia Tech)
Causal Framework for Building Resilient Supply Chains
Karthika Mohan (Oregon State University)
Room Maastricht
Towards Causal LLM Agents: Causal Reasoning in LLMs
Zhijing Jin (University of Toronto)
Mining Causality: AI-Assisted Search for Instrumental Variables
Sukjin Han (University of Bristol)
Understanding Problems of Place Through Data: Spatial Causal Inference in Public Administration Research
Stephen Kleinschmit, Ph.D. (Northwestern University)
Causal Discovery for Product Analytics
Sean Taylor (Motif Analytics)
18.30 – 19.30
Room Copenhagen
Keynote

Susan Athey (Stanford University)
13:00–14.20
Room Copenhagen
Using Causal Machine Learning to Optimize Social Welfare Policies in Kazakhstan: A Case Study on Cash Transfers and Child-Related Outcomes
Alibi Jangeldin (NITEC JSC)
Identifying Successful Interventions Through Break Detection: A New Machine Learning Approach for Generating Hypotheses
Patrick Klösel (Potsdam Institute for Climate Impact Research)
Conflict in a Warming World: How Climate Shocks Impact Rebel Demands and Peace Agreement Outcomes
Elisa D'Amico (University of St. Andrews)
The Heterogeneous Effects of Active Labour Market Policies in Switzerland
Federica Mascolo, Nora Bearth, Fabian Muny, Michael Lechner, Jana Mareckova (University of St.Gallen)
Room Maastricht
Mostly Harmless Fixed Effects Regression in Python via PyFixest
Alexander Fischer (Trivago)
Bringing Light to the Threshold: Evaluation of Production Policies Using the Regression Discontinuity Design with Application to LED Manufacturing
Oliver Schacht (University of Hamburg), Philipp Schwarz (Osram), Sven Klaassen (University of Hamburg, Economic AI), Martin Spindler (University of Hamburg, Economic AI)
Causal Machine Learning with Counterfactual Prediction: The User’s Guide
Aurélien Sallin (SWICA), Daniel Ammann (University of Applied Sciences), Tobias Müller (University of Applied Sciences)
A Joint Test of Unconfoundedness and Common Trends
Martin Huber (University of Fribourg), Eva-Maria Oeß (Universität zu Köln)
14:50–16.10
Room Copenhagen
Testing Identification in Mediation and Dynamic Treatment Models
Martin Huber (University of Fribourg), Kevin Kloiber (University of Munich), Lukáš Lafférs (Matej Bel University, Norwegian School of Economics)
Learning Control Variables and Instruments for Causal Analysis in Observational Data
Nicolas Apfel (University of Southampton), Julia Hatamyar (University of York), Martin Huber (University of Fribourg), Jannis Kueck (Heinrich Heine University Düsseldorf)
Manipulation Tests in Regression Discontinuity Design: The Need for Equivalence Testing
Jack Fitzgerald (Vrije Universiteit Amsterdam)
Arguing for Covariate Balance
Jeffrey J. Harden (University of Notre Dame)
Room Maastricht
NovoGraphs: A Benchmark for Evaluating the Generalizability of LLM-Based Graph Discovery
Ashutosh Srivastava (IIIT Hyderabad), Lokesh Nagalapatti (IIT Bombay), Gautam Jajoo (Microsoft Research), Amit Sharma (Microsoft Research)
The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
George Gui (Columbia Business School), Olivier Toubia (Columbia Business School)
Teaching Transformers Causal Reasoning Through Axiomatic Training
Aniket Vashishtha (UIUC), Abhinav Kumar (MIT), Atharva Pandey (Microsoft Research), Abbavaram Gowtham Reddy (IIT), Vineeth N Balasubramanian (IIT), Amit Sharma (Microsoft Research)
Implicit Personalization in Language Models: A Systematic Study
Zhijing Jin (MPI & ETH Zürich), Nils Heil (TUM), Jiarui Liu (Carnegie Mellon), Shehzaad Dhuliawala (ETH Zürich), Yahang Qi (ETH Zürich), Bernhard Schölkopf (MPI), Rada Mihalcea (University of Michigan), Mrinmaya Sachan (ETH Zürich)
16:40–18.00
Room Copenhagen
Industry Roundtable (to be announced)
Room Maastricht
Leveraging a Natural Experiment to Estimate the Impact of Customer Satisfaction in Contact Centers
Felipe Bahamonde (LATAM Airlines), Paolo Gorgi (acmetric), Hyeokmoon Kweon (Vrije Universiteit Amsterdam), Leandro Magga (LATAM Airlines), Sebastián Orellana (LATAM Airlines)
Regression Adjustments for Experimental Designs in Two-Sided Marketplaces
Timothy Sudijono (Stanford University), Lihua Lei (Stanford University)
Data Leakage in Recommendation System A/B Tests
Roshni Sahoo (Stanford University), Jennifer Brennan (Google Research), Zak Mhammedi (Google Research), Jean Pouget-Abadie (Google Research)
Large Scale Longitudinal Experiments: Estimation and Inference
Apoorva Lal (Netflix), Alex Fischer (Trivago), Matthew Wardrop (Netflix)
18:30–20.00
Room Copenhagen
Causal Bandits Live Podcast
Moderation:
Alexander Molak (Causal Bandits Podcast)
Guest:
Ciarán M.Gilligan-Lee (Spotify)
Keynote02

"Causal inference in economics is fundamentally about determining the impact of one variable on another, a core task in policy evaluation."

Susan Athey

Professor Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford, and Harvard. She is an elected member of the National Academy of Science and is the recipient of the John Bates Clark Medal, awarded by the American Economics Association to the economist under 40 who has made the greatest contributions to thought and knowledge.

Her research is in the areas of the economics of digitization, marketplace design, and the intersection of machine learning and econometrics. She has studied a range of application areas, including timber auctions, online advertising, the news media, and the application of technology for social impact.

We are very happy to welcome Professor Athey to her keynote on November 5, 6.00PM CET, 9.00AM PDT (Zoom, register above).

Live Podcast03

We are happy to welcome Alexander Molak, Host of the Causal Bandits Podcast, live-interviewing Ciarán M.Gilligan-Lee, Head of the Causal Inference Research Lab at Spotify.

Aleksander Molak

Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. Aleksander is the founder and host of the Causal AI-centered Causal Bandits Podcast.

Ciarán Gilligan-Lee
is the Head of the Causal Inference Research Lab at Spotify and an Honorary Associate Professor at University College London. His research focuses on causal inference and its applications, with notable contributions in the fields of healthcare, privacy, and decision-making.
His work has been described as a "breakthrough" by Newsweek, and MIT Technology Review noted that it "could supercharge medical AI" and "is set to improve automated decision making in finance, healthcare, ad targeting, and more."

The live podcast will be streamed on November 6, 6.30PM CET, 9.30AM PDT (Zoom, register above).

Ciarán Gilligan-Lee
Who it's for04

Research meets practice.

Students and Researchers

Explore cutting-edge methodologies and engage in discussions about the role and impact of causality in machine learning.

Industry Professionals

Bridge the gap between theory and practice by exploring real-world applications of causal data science.

Policy Makers

Understand the role of causality for fairness, robustness, and discrimination in policy-making.

Image: ID 241391100 © Evgeny Turaev | Dreamstime.com

About the Meeting05

Providing an open space to advance the frontier of causal data science.

The Causal Data Science Meeting was founded in 2020 and thought as as a small-scale workshop for 50 attendees. However, already in its first year, CDSM received an overwhelming response of 900 pre-registrations, which encouraged us to continue the event annually. We aim to create a friendly, efficient, and constructive environment for academics and practitioners to exchange ideas on all causality-related topics in data science and machine learning. We strive to maintain transparency in our non-profit goal and use all sponsorships received to cover smaller expenses and PhD research.

3.500+

Participants since 2020

48%

Accepted presentations

Past keynote speakers

Judea Pearl
UCLA
Silivia Chiappa
Google DeepMind
Guido Imbens
Stanford University
Sara Magliacane
Amsterdam Machine
Learning Lab
Elias Bareinboim
Columbia University
Sean Taylor
Lyft
Dominik Janzing
Amazon Research
Voices07

What participants say

I am thrilled to see people from different disciplines come together.

Guido Imbens
Stanford University, Recipient of the 2021 Nobel Memorial Prize in Economic Sciences

The next revolution will be even more impactful upon realizing that data science is the science of interpreting reality, not of summarizing data.

Judea Pearl
UCLA, Recipient of the Turing Award in 2011, Author of The Book of Why

Slider Left Arrow
Slider Right Arrow
Organisers08

The Causal Data Science Meeting 2024 is jointly organized by researchers from Maastricht University, Netherlands, and Copenhagen Business School, Denmark.

Paul Hünermund
Assistant Professor, Copenhagen Business School
Jermain Kaminski
Assistant Professor, Maastricht University
Carla Schmitt
PhD Candidate, Maastricht University
Beyers Louw
Assistant Professor, Rotterdam School of Management
Maastricht University Tapijn