publications

2026

1. ICML Spotlight

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

   Felix Schur, Niklas Pfister, Peng Ding, and 2 more authors

   In International Conference on Machine Learning (ICML), 2026

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   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 experimental conditions (environments) but do not observe them jointly—we either observe the covariates or the outcome. Under appropriate regularity conditions, the problem can be cast as an instrumental variable (IV) regression with the environment acting as a (possibly high-dimensional) instrument. When there are many environments but only a few observations per environment, standard two-sample IV estimators fail to be consistent. We propose a GMM-type estimator (SplitUP) based on cross-fold sample splitting of the instrument–covariate sample and prove that it is consistent as the number of environments grows but the sample size per environment remains constant. We further extend the method to sparse causal effects via L1-regularized estimation and post-selection refitting.

   @inproceedings{schur2026manyexperiments,
     title = {Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?},
     author = {Schur, Felix and Pfister, Niklas and Ding, Peng and Mukherjee, Sach and Peters, Jonas},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2026},
   }

2. UAI

   The Price of Knowledge: Optimal Algorithms for Costly Bandits

   Felix Schur, Jesus Lago, and Tanner Fiez

   In 42nd Conference on Uncertainty in Artificial Intelligence (UAI), 2026

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   We study stochastic bandits in which observing a reward is optional but incurs an action-dependent cost. This setting captures applications where feedback acquisition (e.g., human evaluation or randomized testing) is expensive, and the learner must trade off exploration, exploitation, and observation cost. We formulate regret to include both reward loss and the cumulative cost of requested observations. Our first result is structural: for minimizing regret, it is without loss of generality to consider two-phase policies that first request observations during an exploration phase and then commit to a single action without further observations. Building on this reduction, we introduce two cost-sensitive complexity measures that extend maximum information gain: a cost-adjusted information gain for minimax analysis, and a cost- and gap-adjusted information gain for instance-dependent analysis. Using these quantities, we develop two Gaussian-process-based algorithms, C3-GP and GP-C-LUCB, and derive regret upper bounds for correlated-action settings with heterogeneous (i.e., action-dependent) observation costs. In the finite independent-action setting, we further prove matching lower and upper bounds (up to constants/logarithmic factors), yielding a tight characterization of both minimax and instance-dependent regret in terms of the proposed cost-aware complexity measures.

   @inproceedings{schur2026priceofknowledge,
     title = {The Price of Knowledge: Optimal Algorithms for Costly Bandits},
     author = {Schur, Felix and Lago, Jesus and Fiez, Tanner},
     booktitle = {42nd Conference on Uncertainty in Artificial Intelligence (UAI)},
     year = {2026},
   }

3. ICML Workshop

   Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity

   Felix Schur

   In ICML Workshop on Demos (DEMO), 2026

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   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 action-free but tagged with demonstrator identity. We assume that each demonstrator follows a distinct policy, while the environment dynamics are shared across demonstrators and identity affects the next observation only through the chosen action. Under these assumptions, the conditional next-observation distribution is a mixture of latent action-conditioned transition kernels with demonstrator-specific mixing weights. We show that this induces, for each state, a column-stochastic nonnegative matrix factorization of the observable conditional distribution. Using sufficiently scattered policy diversity and rank conditions, we prove that the latent transitions and demonstrator policies are identifiable up to permutation of the latent action labels. We extend the result to continuous observation spaces via a Gram-determinant minimum-volume criterion, and show that continuity of the transition map over a connected state space upgrades local permutation ambiguities to a single global permutation. A small amount of labeled action data then suffices to fix this final ambiguity. These results establish demonstrator diversity as a principled source of identifiability for learning latent actions and dynamics from offline RL data.

   @inproceedings{schur2026latentactions,
     title = {Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity},
     author = {Schur, Felix},
     booktitle = {ICML Workshop on Demos (DEMO)},
     year = {2026},
   }

2025

1. NeurIPS

   Transferring Causal Effects using Proxies

   Manuel Iglesias-Alonso, Felix Schur, Julius Kügelgen, and 1 more author

   In Advances in Neural Information Processing Systems (NeurIPS), 2025

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   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 different domains. We assume that we have access to a proxy of the hidden confounder and that all variables are discrete or categorical. We propose methodology to estimate the causal effect in the target domain, where we assume to observe only the proxy variable. Under these conditions, we prove identifiability (even when treatment and response variables are continuous). We introduce two estimation techniques, prove consistency, and derive confidence intervals. The theoretical results are supported by simulation studies and a real-world example studying the causal effect of website rankings on consumer choices.

   @inproceedings{iglesias2025transferring,
     title = {Transferring Causal Effects using Proxies},
     author = {Iglesias-Alonso, Manuel and Schur, Felix and von K{\"u}gelgen, Julius and Peters, Jonas},
     booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
     year = {2025},
   }

2. JRSSB

   DecoR: Deconfounding Time Series with Robust Regression

   Felix Schur, Pio Blieske, and Jonas Peters

   Journal of the Royal Statistical Society Series B: Statistical Methodology, 2025

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   Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency domain. We introduce Deconfounding by Robust regression (DecoR), a novel approach that estimates the causal effect using robust linear regression in the frequency domain. We consider two robust regression techniques and provide improved bounds on their estimation errors. Applying these results to DecoR, we prove, under suitable assumptions, upper bounds for the estimation error of DecoR that imply consistency. We demonstrate DecoR’s effectiveness through experiments on both synthetic and real-world data from Earth system science. The simulation experiments furthermore suggest that DecoR is robust with respect to model misspecification.

   @article{schur2025decor,
     title = {DecoR: Deconfounding Time Series with Robust Regression},
     author = {Schur, Felix and Blieske, Pio and Peters, Jonas},
     journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
     year = {2025},
   }

2024

1. arXiv

   Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs

   Silvana Tiedemann, Jorge Sanchez Canales, Felix Schur, and 4 more authors

   2024

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   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 autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis.

   @misc{tiedemann2024identifyingelasticitiesautocorrelatedtime,
     title = {Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs},
     author = {Tiedemann, Silvana and Canales, Jorge Sanchez and Schur, Felix and Sgarlato, Raffaele and Hirth, Lion and Ruhnau, Oliver and Peters, Jonas},
     year = {2024},
     archiveprefix = {arXiv},
     primaryclass = {econ.EM},
   }

2023

1. UAI

   Lifelong Bandit Optimization: No Prior and No Regret

   Felix Schur, Parnian Kassraie, Jonas Rothfuss, and 1 more author

   In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), Aug 2023

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   Machine learning algorithms are often repeatedly applied to problems with similar structure over and over again. We focus on solving a sequence of bandit optimization tasks and develop LIBO, an algorithm which adapts to the environment by learning from past experience and becomes more sample-efficient in the process. We assume a kernelized structure where the kernel is unknown but shared across all tasks. LIBO sequentially meta-learns a kernel that approximates the true kernel and solves the incoming tasks with the latest kernel estimate. Our algorithm can be paired with any kernelized or linear bandit algorithm and guarantees oracle optimal performance, meaning that as more tasks are solved, the regret of LIBO on each task converges to the regret of the bandit algorithm with oracle knowledge of the true kernel. Naturally, if paired with a sublinear bandit algorithm, LIBO yields a sublinear lifelong regret. We also show that direct access to the data from each task is not necessary for attaining sublinear regret. We propose F-LIBO, which solves the lifelong problem in a federated manner.

   @inproceedings{schur23a,
     title = {Lifelong Bandit Optimization: No Prior and No Regret},
     author = {Schur, Felix and Kassraie, Parnian and Rothfuss, Jonas and Krause, Andreas},
     booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI)},
     pages = {1847--1857},
     year = {2023},
     month = aug,
     publisher = {PMLR},
   }