no code implementations • ICML 2020 • Daniel Kumor, Carlos Cinelli, Elias Bareinboim
We develop a a new polynomial-time algorithm for identification in linear Structural Causal Models that subsumes previous non-exponential identification methods when applied to direct effects, and unifies several disparate approaches to identification in linear systems.
no code implementations • 30 Apr 2022 • Lang Liu, Carlos Cinelli, Zaid Harchaoui
Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component.
1 code implementation • 26 Dec 2021 • Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias.