publications

2024

1. arXiv

   DecoR: Deconfounding Time Series with Robust Regression

   Felix Schur, and Jonas Peters

   2024

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   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 time series, which are confounded by a third, unobserved time series. Assuming spectral sparsity of the confounder, we show how in the frequency domain this problem can be framed as an adversarial outlier problem. We introduce Deconfounding by Robust regression (DecoR), a novel approach that estimates the causal effect using robust linear regression in the frequency domain. Considering two different robust regression techniques, we first improve existing bounds on the estimation error for such techniques. Crucially, our results do not require distributional assumptions on the covariates. We can therefore use them in time series settings. Applying these results to DecoR, we prove, under suitable assumptions, upper bounds for the estimation error of DecoR that imply consistency. We show DecoR’s effectiveness through experiments on synthetic data. Our experiments furthermore suggest that our method is robust with respect to model misspecification.

   @misc{schur2024decor,
     title = {DecoR: Deconfounding Time Series with Robust Regression},
     author = {Schur, Felix and Peters, Jonas},
     year = {2024},
     eprint = {2406.07005},
     archiveprefix = {arXiv},
     primaryclass = {stat.ML},
     dimensions = {true},
   }

2. 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},
     eprint = {2409.15530},
     archiveprefix = {arXiv},
     primaryclass = {econ.EM},
     dimensions = {true},
   }

2023

1. UAI

   Lifelong Bandit Optimization: No Prior and No Regret

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

   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.

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