5 papers
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4
Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences).…
Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Tomàs Garriga, Alejandro Almodóvar, Axel Brando +3
Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than uniformly over the entire action s…
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…
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…