PAST EDITIONS / 2023

CDSM 2023.

7–8 November 2023Online

Keynote

Dominik JanzingAmazon Research
All past editions

7 November 2023

DAY 01 / ONLINE

Functional Causal Bayesian Optimization

Limor Gultchin (University of Oxford), Virginia Aglietti (Google DeepMind), Alexis Bellot (Google DeepMind), Silvia Chiappa (Google DeepMind)

Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation

Patrick Rehill (Australian National University)

Price Elasticity Estimation using Image and Text Data

Victor Chernozhukov (MIT), Sven Klaassen (University of Hamburg), Martin Spindler (University of Hamburg), Jan Teichert-Kluge (University of Hamburg)

Data-Driven Investment Decisions for Venture Capital: A Causal Machine Learning Approach

Abdurahman Maarouf (LMU Munich), Jonas Schweisthal (LMU Munich), Stefan Feuerriegel (LMU Munich)

Overcoming Organizational Challenges for Causal Inference Adoption at Dream11

Namita Porwal (Dream11), Vinod Reddy (Dream11)

Implementing Causal AI in Industry: Organisational Challenges and Best Practices

Hendrik Jacobsen (Schwäbische Werkzeugmaschinen GmbH)

Bridging the Gap Between Predictive Performance and Decision Making: Inverse Probability Weighting for Accuracy Estimation

Mones Raslan (Zalando SE), Stefan Birr (Zalando SE), Patrick Doupe (Zalando SE), Tim Januschowski (Zalando SE)

Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation

Dmitri Goldenberg (Booking.com)

Not Causal or Descriptive But Some Secret, Other Thing: Entropy as a Criterion for Causal Learning

Robert Kubinec (New York University Abu Dhabi)

Engagement Dynamics in mHealth

Yikun Jiang (University of California, Berkeley), Kosuke Uetake (Yale University), Nathan Yang (Cornell University)

Dynamic Pricing in B2B Markets

Alexander MacKay (Harvard Business School), Menna Hassan (Harvard Business School), Rembrand Koning (Harvard Business School)

Neuro-Causal Models

Bryon Aragam (University of Chicago), Pradeep Ravikumar (Carnegie Mellon University)

Policy Learning under Biased Sample Selection

Lihua Lei (Stanford University), Rashni Sahoo (Stanford University), Stefan Wager (Stanford University)

Evaluating Instrument Validity Using the Principle of Independent Mechanism

Patrick F. Burauel (Caltech)

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects

Wenlong Ji (Stanford University), Lihua Lei (Stanford University), Asher Spector (Stanford University)

Causally Sound Priors for Binary Experiments

Nicholas J. Irons (University of Washington), Carlos Cinelli (University of Washington)

Keynote

Finding Root Causes via Decomposing Complex Systems into Mechanisms

Dominik Janzing (Amazon Research)

8 November 2023

DAY 02 / ONLINE

Causally Learning an Optimal Rework Policy

Oliver Schacht (University of Hamburg), Sven Klaassen (University of Hamburg & Economic AI), Philipp Schwarz (University of Hamburg & OSRAM), Martin Spindler (University of Hamburg & Economic AI), Daniel Gruenbaum (OSRAM), Sebastian Imhof (OSRAM)

The Perks and Perils of Machine Learning in Business Research

Tom Dudda (Dresden University of Technology), Lars Hornuf (Dresden University of Technology)

Additive Causal Bandits with Unknown Graph

Alan Malek (Google DeepMind), Virginia Aglietti (Google DeepMind), Silvia Chiappa (Google DeepMind)

RCTrep: An R Package for the Validation of Estimates of the Average Treatment Effect

Lingjie Shen (Tilburg University), Gijs Geleijnse (IKNL), Maurits Kaptein (JADS)

Transportability for Bandits with Data from Different Environments

Alexis Bellot (Google DeepMind), Alan Malek (Google DeepMind), Silvia Chiappa (Google DeepMind)

Identifying Dynamic LATEs with a Static Instrument

Bruno Ferman (Sao Paulo School of Economics), Otávio Tecchio (Sao Paulo School of Economics)

When Is Heterogeneity Useless? An Analysis of Targeting Potential in Studies with Multiple Arms

Anya Shchetkina (University of Pennsylvania, Wharton), Ron Berman (University of Pennsylvania, Wharton)

Causal Theories and Structural Data Representations for Improving Out-of-Distribution Classification

Donald Martin (Google Research), David Kinney (Yale University)

Identification and Estimation of Discrete Choice Models with Spillovers Using Partial Network Data

Shuo Qi (Southern Methodist University)

Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification

Carlos Fernández-Loría (Hong Kong University of Science and Technology), Jorge Loría (Purdue University)

Fixed Effects and Causal Inference

Daniel Millimet (Southern Methodist University & IZA), Marc Bellemare (University of Minnesota)

Poisson Regression Under Heterogeneous Treatment Effects

Georgy Kalashnov (Stanford University), Lihua Lei (Stanford University)

Causal Reasoning and LLMs: A New Frontier

Emre Kıcıman (Microsoft Research), Robert Ness (Microsoft Research), Amit Sharma (Microsoft Research), Chenhao Tan (University of Chicago)

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal

Matej Zečević (TU Darmstadt), Moritz Willig (TU Darmstadt), Devendra Singh Dhami (TU Darmstadt), Kristian Kersting (TU Darmstadt)

Can Large Language Models Infer Causation from Correlation?

Zhijing Jin (Max Planck Institute for Intelligent Systems), Jiarui Liu (University of Michigan), Zhiheng Lyu (University of Hong Kong), Spencer Poff (Meta AI), Mrinmaya Sachan (ETH Zürich), Rada Mihalcea (University of Michigan), Mona Diab (Meta AI), Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)

Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task

Kenneth Li (Harvard University), Aspen Hopkins (MIT), David Bau (Northeastern University), Fernanda Viégas (Harvard University), Hanspeter Pfister (Harvard University), Martin Wattenberg (Harvard University)

Roundtable

Experimentation and A/B Testing

Amit K. Mondal (American Express), Benjamin Skrainka (eBay), Iavor I. Bojinov (Harvard Business School), Somit Gupta (Microsoft), Hosts: Victor Zitian Chen (Fidelity Investments), Scott Macmillan (Fidelity Investments)

Roundtable

From Causal Science to Prescriptive Intelligence

Patrick Doupe (Zalando), Thomas Baudel (IBM), Victor Lo (Fidelity Investments), Hosts: Victor Zitian Chen (Fidelity Investments), Scott Macmillan (Fidelity Investments)