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Found 5 papers, 1 papers with code
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
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.
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
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.
Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects.
Bayesian causal inference via probabilistic program synthesis
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.
Measuring and Characterizing Generalization in Deep Reinforcement Learning
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.
