no code implementations • 23 Feb 2021 • Sam Witty, David Jensen, Vikash Mansinghka
This paper introduces simulation-based identifiability (SBI), a procedure for testing the identifiability of queries in Bayesian causal inference approaches that are implemented as probabilistic programs.
1 code implementation • 17 Dec 2020 • Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J. Giguere, Yuriy Brun
To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.
no code implementations • ICML 2020 • Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects.
no code implementations • 30 Oct 2019 • Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka
This approach makes it straightforward to incorporate data from atomic interventions, as well as shift interventions, variance-scaling interventions, and other interventions that modify causal structure.
no code implementations • 7 Dec 2018 • Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen
We re-examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states.