5 citations · 5 across the 9 of their papers we have counts for
7 papers · 1 filter
Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence
Eddie Conti, Claudio Daka, Álvaro Parafita +3
Feature importance Methods (FIMs) are widely used in Explainable AI to interpret model predictions, yet attribution scores alone often provide limited insight into the underlying r…
Measuring Explainer Stability via Attribution Separability
Eddie Conti, Álvaro Parafita, Axel Brando
Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models. However, most methods can produce variable attribution scor…
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
Tomàs Garriga, Gerard Sanz, Eduard Serrahima de Cambra +1
The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to under…
Exactly Computing do-Shapley Values
R. Teal Witter, Álvaro Parafita, Tomas Garriga +4
Structural Causal Models (SCM) are a powerful framework for describing complicated dynamics across the natural sciences. A particularly elegant way of interpreting SCMs is do-Shapl…
CID: Measuring Feature Importance Through Counterfactual Distributions
Eddie Conti, Álvaro Parafita, Axel Brando
Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous methods exist, the lack of a defin…
Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
Álvaro Parafita, Tomas Garriga, Axel Brando +1
Among explainability techniques, SHAP stands out as one of the most popular, but often overlooks the causal structure of the problem. In response, do-SHAP employs interventional qu…
Probing the Embedding Space of Transformers via Minimal Token Perturbations
Eddie Conti, Alejandro Astruc, Alvaro Parafita +1
Understanding how information propagates through Transformer models is a key challenge for interpretability. In this work, we study the effects of minimal token perturbations on th…