# CDSM 2020 — Programme archive

Source: https://causalscience.org/archive-2020

PAST EDITIONS / 2020

## CDSM 2020.

11–12 November 2020 Online

Keynotes

Sean Taylor Lyft, Rideshare Labs

Elias Bareinboim Columbia University, Department of Computer Science

[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 / 11 November](https://causalscience.org/archive-2020#day-1) [Day 2 / 12 November](https://causalscience.org/archive-2020#day-2)

### 11 November 2020

DAY 01 / ONLINE

#### DoWhy: An end-to-end library for causal inference

Amit Sharma (Microsoft Research), Emre Kiciman

#### Causal mediation analysis with double machine learning

Helmut Farbmacher, Martin Huber (University of Fribourg, Faculty of Management, Economics and Social Sciences), Lukáš Lafférs, Henrika Langen, Martin Spindler

#### Double machine learning and bad controls: A cautionary tale

Itamar Caspi (Bank of Israel, Research Department), Paul Hünermund

#### How to (try to) expand observational causal inference in industry

Patrick Doupe (Zalando), Christopher Gandrud

#### Causal inference in an industrial context: Lessons learned from Total

Antoine Bertoncello (Research and Development, Total)

#### A polynomial-time algorithm for learning nonparametric causal graphs

Ming Gao, Yi Ding, Bryon Aragam (University of Chicago, Booth School of Business)

#### Heterogeneous treatment and spillover effects under clustered network interference

Falco J. Bargagli-Stoffi (Harvard University, T.H. Chan School of Public Health), Costanza Tortu, Laura Forastiere

#### A calculus for soft interventions

Juan D. Correa (Columbia University, Department of Computer Science)

#### Challenges and an empirical evaluation framework for text-based confounding adjustment

Galen Weld (University of Washington, Paul G. Allen School of Computer Science and Engineering), Peter West, Maria Glenski, David Arbour, Ryan A. Rossi, Tim Althoff

#### Conformal inference of counterfactuals and individual treatment effects

Lihua Lei (Stanford University, Department of Statistics), Emmanuel J. Candès

#### Asking one question and answering another: When decisions and statistical analysis are not aligned

Ignacio Martinez (Google, The Chief Economist’s Team), Jesse Chandler, Daniel Thal

#### Learning the effect of dexamethasone, remdesivir, and hydroxychloroquine on COVID-19 mortality outside of randomized trials using the stability-controlled quasi-Experiment

Chad Hazlett (University of California, Los Angeles, Department of Statistics), David Ami Wulf, Brian Hill, Brian Montague, Kristine Erlandson, Jeffrey Chiang, Onyebuchi Arah, Bogdan Pasaniuc

Keynote

#### Keynote

Sean Taylor (Lyft, Rideshare Labs)

### 12 November 2020

DAY 02 / ONLINE

#### Targeting fundraising gifts: A causal AI approach

Tobias Cagala, Ulrich Glogowsky, Johannes Rincke, Anthony Strittmatter (University of St. Gallen, Center for Research in Economics and Statistics)

#### Challenges in causal data science at the enterprise

Ohad Levinkron (Vian.ai)

#### Validating treatment effects estimated from observational data: a two- step approach

Lingjie Shen (Tilburg University, Department of Methodology and Statistics), Erick Visser, Felice van Erning, Gijs Geleijnse, Maurits Kaptein

#### Causality in data science education

Karsten Lübke (FOM University of Applied Sciences, Institute for Empirical Research and Statistics), Matthias Gehrke, Jörg Horst, Gero Szepannek

#### Estimating the earnings and employment effects of the minimum wage through differences in exposure across US counties

Jesse Wursten (KU Leuven, Faculty of Economics and Business)

#### Dynamical systems theory for causal inference with application to synthetic control methods

Yi Ding (University of Chicago, Department of Computer Science), Panos Toulis

#### Algebraic ground truth inference for counterfactual assumptions

Andres Corrada-Emmanuel (swoop.com), Aditya Chaganti, Edward Pantridge, Edward Zahrebelski, Simeon Simeonov

#### Causal Inference with nonclassical measurement error in the dependent variable

Daniel Millimet (Southern Methodist University, Department of Economics)

#### mr_uplift: Machine learning Uplift package

Sam Weiss (Ibotta.com)

#### Counterfactual demand predictions: Deep learning with microeconomic theory

Dong Soo Kim, Chul Kim, Mingyu (Max) Joo (University of California, Riverside, School of Business), Hai Che

#### An omitted variable bias framework for sensitivity analysis of instrumental variables

Carlos Cinelli (University of California, Los Angeles, Department of Statistics), Chad Hazlett

#### Latent stratification for advertising experiments

Ron Berman (University of Pennsylvania, Wharton School, Department of Marketing), Elea McDonnell Feit

Keynote

#### Keynote

Elias Bareinboim (Columbia University, Department of Computer Science)

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)