# CDSM 2021 — Programme archive

Source: https://causalscience.org/archive-2021

PAST EDITIONS / 2021

## CDSM 2021.

15–16 November 2021 Online

Keynotes

Sara Magliacane University of Amsterdam & MIT-IBM Watson AI Lab

Guido Imbens Stanford University

[All past editions](https://causalscience.org/archive.html)

Browse editions [2025](https://causalscience.org/archive-2025.html) [2024](https://causalscience.org/archive-2024.html) [2023](https://causalscience.org/archive-2023.html) [2022](https://causalscience.org/archive-2022.html) [2021](https://causalscience.org/archive-2021.html) [2020](https://causalscience.org/archive-2020.html)

Programme [Day 1 / 15 November](https://causalscience.org/archive-2021#day-1) [Day 2 / 16 November](https://causalscience.org/archive-2021#day-2)

### 15 November 2021

DAY 01 / ONLINE

#### Self-fulfilling Bandits: Endogeneity spillover and dynamic selection in algorithmic decision-making

Xiaowei Zhang (Hongkong University)

#### Off-policy learning of dynamic content promotions

Joel Persson (ETH Zürich)

#### Estimating returns to special education: Combining machine learning and text analysis to address confounding

Aurélien Sallin (St. Gallen University)

#### What’s on the telly? Causality for recommender systems in public-service media corporations

Jordi Mur (University of Barcelona)

#### Structural causal models are (solvable by) credal networks

Alessandro Antonucci (Dalle Molle Institute for Artificial Intelligence Research (IDSIA))

#### Estimating the probabilities of causation via deep monotonic twin networks

Ciarán Lee (Spotify Research)

#### Double machine learning for sample selection models

Martin Huber (University of Fribourg)

#### Positivity violation detection and explainability

Hanan Shteingart (Vian.ai)

#### Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment

Jason Poulos (Harvard Medical School)

#### Crime and mismeasured punishment: Marginal treatment effect with misclassification

Vitor Possebom (Yale University)

#### When should we (not) interpret linear IV estimands as LATE?

Tymon Sloczynski (Brandeis University)

#### Preferences and productivity in organizational matching: Theory and empirics from internal labor markets

Bo Cowgill (Columbia Business School)

#### Experimentation and startup performance: Evidence from A/B testing

Rem Koning (Harvard Business School)

#### The paper of how: Estimating treatment effects using the front-door criterion

Marc Bellemare (University of Minnesota)

#### Causal-driven machine learning at Uber scale: A case study

Okke van der Wal (Uber)

#### Generalizing experimental results by leveraging knowledge of mechanisms

Carlos Cinelli (University of Washington)

Keynote

#### Keynote

Sara Magliacane (University of Amsterdam & MIT-IBM Watson AI Lab)

### 16 November 2021

DAY 02 / ONLINE

#### The impact of the #MeToo movement on language at court: A text-based causal inference approach

Henrika Langen (University of Fribourg)

#### Firm incentives and consumer adaption in a multi unit auction

Simon Schulten (Düsseldorf Institute for Competition Economics (DICE))

#### Drawing (causal) conclusions from data – some evidence

Karsten Lübke (FOM University of Applied Sciences)

#### End-to-end causal analysis in Python with cause2e

Daniel Gruenbaum (Osram)

#### The role of the propensity score in fixed effect models

Dmitry Arkhangelsky (Center for Monetary and Financial Studies (CEMFI))

#### The challenges of measuring the impact of interventions in brick-and-mortar stores

Patrick de Oude (Albert Heijn)

#### Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs

Giusi Moffa (University of Basel)

#### Causal inference with proxy variables in Booking.com

Christina Katsimerou (Booking.com)

#### Treatment effects in strategic management: with an application to choosing early stage venture capital

Jorge Guzman (Columbia Business School)

#### Deep learning for individual heterogeneity: An automatic inference framework

Max Farrell (The University of Chicago Booth School of Business)

#### Causal knowledge graph: A demonstration

Victor Chen (University of North Carolina at Charlotte)

#### An empirical analysis of intra-firm product substitutability in fashion retailing

Nathan Yang (Cornell University)

#### The importance of being causal

Iavor Bojinov (Harvard Business School)

#### Desiderata for representation learning: A causal perspective

Yixin Wang (University of Michigan)

#### What experimental designs justify two-way-fixed-effects regression estimators

Lihua Lei (Stanford University)

#### CausalML: A Python package for uplift modeling and causal inference with machine learning

Zhenyu Zhao (Tencent, Toyota Research), Totte Harinen (Tencent, Toyota Research)

Keynote

#### A Design Approach to Synthetic Controls

Guido Imbens (Stanford University)

Browse editions [2025](https://causalscience.org/archive-2025.html) [2024](https://causalscience.org/archive-2024.html) [2023](https://causalscience.org/archive-2023.html) [2022](https://causalscience.org/archive-2022.html) [2021](https://causalscience.org/archive-2021.html) [2020](https://causalscience.org/archive-2020.html)