1 citations · 1 across the 7 of their papers we have counts for
8 papers
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +7
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy va…
Partial Identification Learning with Categorical Treatments for Individualized Treatment Rules
Johannes Hruza, Paweł Morzywołek, Jakob Zeitler +2
We develop a partial identification learning framework for individualized treatment rules (ITRs) with categorical treatments, outcomes, and instrumental variables. Rather than rely…
Search Strategies for Self-driving Laboratories with Pending Experiments
Hao Wen, Jakob Zeitler, Connor Rupnow
Self-driving laboratories (SDLs) consist of multiple stations that perform material synthesis and characterisation tasks. To minimize station downtime and maximize experimental thr…
Intervention Generalization: A View from Factor Graph Models
Gecia Bravo-Hermsdorff, David S. Watson, Jialin Yu +2
One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapp…
Non-parametric identifiability and sensitivity analysis of synthetic control models
Jakob Zeitler, Athanasios Vlontzos, Ciaran M. Gilligan-Lee
Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situati…
The Causal Marginal Polytope for Bounding Treatment Effects
Jakob Zeitler, Ricardo Silva
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model. Nevertheless, we can ask for partial identification, which usually boils…
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler +2
Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system. As it involves reasonin…