Past Editions / 2025
CDSM 2025.
Keynote
Stefan FeuerriegelLMU
November 12, 2025
Day 01 / Online
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Causal Inference for Root Cause Analysis in Manufacturing: A causaLens Approach
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder
Substitution Effects in Fashion Retail Demand
Seeking External Advice – Which Firms in Emerging Markets Hire External Consultants?
Do Zombie Firms Really Cause Congestion?
Policy Learning in Practice: Simulation Evidence and a Case Study of Optimal Allocation of Subsidised Health Insurance
Convergence of Q-Learning Under Relative Ignorability
Precision Gains from Temporal Switchback Designs for Seat-Ancillary Pricing Experiments at LATAM Airlines
Improving Empirical Models in Strategic Management Research with Double Machine Learning
Causal AI Scientist: Facilitating Causal Data Science with Large Language Models
Causal Bandits Special: Do Heterogeneous Treatment Effects Exist?
DAG It: Drawing Assumptions Before Conclusions Changes Results
Debiased Front-Door Learners for Heterogeneous Effects
Non-overlap Average Treatment Effect Bounds
Compound Causal Selection Decisions: An Almost SURE Approach
November 13, 2025
Day 02 / Online
Avoiding Mistakes in Measuring the Impact of AI
Treatment Effect Estimators as Weighted Outcomes
Federated Causal Inference beyond Meta-Analysis in RCTs and Observational Studies
Quantile Individualized Average Treatment Effect
Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation
Causal Science Assistant
Unsupervised Discovery of Causal Mechanisms for Management Research
Sensitivity Analysis for Quasi-Experimental Methods
Open Causal: a FAIR Platform for Causal Graphs
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Compound Causal Inference for Binomial Outcomes
Echoes of the Ping: How Push Notifications Shape App Engagement and its Implications for Targeting
The Impacts of Optimal Bandwidths in Regression Discontinuity Design
Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies
Are Cash Reallocations Effective Amid Multiple Shocks? A Double Selection Approach
Forests for Differences: Robust Causal Inference Beyond Parametric DiD
A Debiased Estimator for the Mediation Functional in Ultra-High Dimensional Setting in the Presence of Interaction Effects
Discovering Causal Relationships Between Time Series with Spatial Structure
The Effects of Flipped Classrooms in Higher Education: A Causal Machine Learning Analysis
Causal Mediation in Natural Experiments
Leveraging Double/Debiased Machine Learning to Improve Meta-Analyses
Generating Impact with Causal ML: Applications, Strengths, and New Opportunities
Power Analysis is Essential: High-Powered Tests Suggest Minimal to No Effect of Rounded Shapes on Click-Through Rates
Interpretable Personalization in Large-Scale Digital Experiments
Adaptive Experimentation: Bandits & Paid Marketing
Enhancing Engagement Metric Sensitivity in Online Experiments via Machine Learning-Based Variance Reduction
Where a presentation lists one name, only the presenting author is shown; co-authors are not recorded for every year.