Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. - Julius von Kügelgen; Nikita Agarwal; Jakob Zeitler; Afsaneh Mastouri; Bernhard Schölkopf (CoRR, 2021)

The Causal Marginal Polytope for Bounding Treatment Effects. - Jakob Zeitler; Ricardo Silva 001 (CoRR, 2022)

Stochastic Causal Programming for Bounding Treatment Effects. - Kirtan Padh; Jakob Zeitler; David S. Watson; Matt J. Kusner; Ricardo Silva 001; Niki Kilbertus (CLeaR, 2023)

Non-parametric identifiability and sensitivity analysis of synthetic control models. - Jakob Zeitler; Athanasios Vlontzos; Ciarán Mark Gilligan-Lee (CLeaR, 2023)

Intervention Generalization: A View from Factor Graph Models. - Gecia Bravo Hermsdorff; David S. Watson; Jialin Yu; Jakob Zeitler; Ricardo Silva 001 (NeurIPS, 2023)

Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators. - Mahdi Eskandari; Lars Puiman; Jakob Zeitler (CoRR, 2023)

Search Strategies for Self-driving Laboratories with Pending Experiments. - Hao Wen; Jakob Zeitler; Connor Rupnow (CoRR, 2023)

Long-run Behaviour of Multi-fidelity Bayesian Optimisation. - Gbètondji J.-S. Dovonon; Jakob Zeitler (CoRR, 2023)

Physical benchmarks for testing algorithms. - Jakob Zeitler (Nat. Mac. Intell., 2025)