Past Editions / 2021
CDSM 2021.
Keynotes
Sara MagliacaneUniversity of Amsterdam & MIT-IBM Watson AI Lab
Guido ImbensStanford University
November 15, 2021
Day 01 / Online
Off-policy learning of dynamic content promotions
Estimating returns to special education: Combining machine learning and text analysis to address confounding
What’s on the telly? Causality for recommender systems in public-service media corporations
Structural causal models are (solvable by) credal networks
Estimating the probabilities of causation via deep monotonic twin networks
Double machine learning for sample selection models
Positivity violation detection and explainability
Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment
Crime and mismeasured punishment: Marginal treatment effect with misclassification
When should we (not) interpret linear IV estimands as LATE?
Preferences and productivity in organizational matching: Theory and empirics from internal labor markets
Experimentation and startup performance: Evidence from A/B testing
The paper of how: Estimating treatment effects using the front-door criterion
Causal-driven machine learning at Uber scale: A case study
Generalizing experimental results by leveraging knowledge of mechanisms
Keynote
November 16, 2021
Day 02 / Online
The impact of the #MeToo movement on language at court: A text-based causal inference approach
Firm incentives and consumer adaption in a multi unit auction
Drawing (causal) conclusions from data – some evidence
End-to-end causal analysis in Python with cause2e
The role of the propensity score in fixed effect models
The challenges of measuring the impact of interventions in brick-and-mortar stores
Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs
Causal inference with proxy variables in Booking.com
Treatment effects in strategic management: with an application to choosing early stage venture capital
Deep learning for individual heterogeneity: An automatic inference framework
Causal knowledge graph: A demonstration
An empirical analysis of intra-firm product substitutability in fashion retailing
The importance of being causal
Desiderata for representation learning: A causal perspective
What experimental designs justify two-way-fixed-effects regression estimators
CausalML: A Python package for uplift modeling and causal inference with machine learning
A Design Approach to Synthetic Controls
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