Combining parametric and nonparametric models for off-policy evaluation. - Omer Gottesman; Yao Liu 009; Scott Sussex; Emma Brunskill; Finale Doshi-Velez (ICML, 2019)

Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning. - Scott Sussex; Caroline Uhler; Andreas Krause 001 (NeurIPS, 2021)

Amortized Inference for Causal Structure Learning. - Lars Lorch; Scott Sussex; Jonas Rothfuss; Andreas Krause 001; Bernhard Schölkopf (NeurIPS, 2022)

Learning Long-Term Crop Management Strategies with CyclesGym. - Matteo Turchetta; Luca Corinzia; Scott Sussex; Amanda Burton; Juan Herrera; Ioannis Athanasiadis; Joachim M. Buhmann; Andreas Krause 001 (NeurIPS, 2022)

Model-based Causal Bayesian Optimization. - Scott Sussex; Anastasiia Makarova; Andreas Krause 001 (CoRR, 2022)

Model-based Causal Bayesian Optimization. - Scott Sussex; Anastasia Makarova; Andreas Krause 001 (ICLR, 2023)

Model-based Causal Bayesian Optimization. - Scott Sussex; Pier Giuseppe Sessa; Anastasiia Makarova; Andreas Krause 001 (CoRR, 2023)

Adversarial Causal Bayesian Optimization. - Scott Sussex; Pier Giuseppe Sessa; Anastasia Makarova; Andreas Krause 001 (ICLR, 2024)

Standardizing Structural Causal Models. - Weronika Ormaniec; Scott Sussex; Lars Lorch; Bernhard Schölkopf; Andreas Krause 001 (ICLR, 2025)