The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

no code implementations • NeurIPS 2019 • Amanda Gentzel, Dan Garant, David Jensen

However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice.

Causal Inference Fairness

Evaluating Causal Models by Comparing Interventional Distributions

no code implementations • 16 Aug 2016 • Dan Garant, David Jensen

The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure.