27 citations · 80 across the 6 of their papers we have counts for
6 papers
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani +2
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in…
Causalvis: Visualizations for Causal Inference
Grace Guo, Ehud Karavani, Alex Endert +1
Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaboration…
Propensity score models are better when post-calibrated
Rom Gutman, Ehud Karavani, Yishai Shimoni
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, es…
A discriminative approach for finding and characterizing positivity violations using decision trees
Ehud Karavani, Peter Bak, Yishai Shimoni
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds…
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
Yishai Shimoni, Ehud Karavani, Sivan Ravid +5
Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available. To be accepted into pract…
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Yishai Shimoni, Chen Yanover, Ehud Karavani +1
Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments…