activity
20242026
collaborators

6 papers

cs.LG2026

Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity

Felix Schur

Can latent actions and environment dynamics be recovered from offline trajectories when actions are never observed? We study this question in a setting where trajectories are actio…

stat.ML2026

Prediction-Intervention Games and Invariant Sets

Linus Kühne, Felix Schur, Jonas Peters

We consider the following two-player game: using observational data, the leader chooses a prediction function for a response variable from given covariates. The follower then r…

stat.ML2026

Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?

Felix Schur, Niklas Pfister, Peng Ding +2

We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates and an outcome under different…

cs.LG2025

Transferring Causal Effects using Proxies

Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen +1

We consider the problem of estimating a causal effect in a multi-domain setting. The causal effect of interest is confounded by an unobserved confounder and can change between the…

stat.ML2024

DecoR: Deconfounding Time Series with Robust Regression

Felix Schur, Jonas Peters

Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. This work focuses on estimating the causal effect between two t…

econ.EM2024

Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs

Silvana Tiedemann, Jorge Sanchez Canales, Felix Schur +4

The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocor…