PAST EDITIONS / 2020

CDSM 2020.

11–12 November 2020Online

Keynotes

Sean TaylorLyft, Rideshare Labs

Elias BareinboimColumbia University, Department of Computer Science

All past editions

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

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

Elias Bareinboim (Columbia University, Department of Computer Science)