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)
November 12, 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)
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