Author name cluster

Urmi Ninad

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7 papers

2 author rows

AAAI Conference 2025 Conference Paper

SPACETIME: Causal Discovery from Non-Stationary Time Series

Sarah Mameche
Lénaïg Cornanguer
Urmi Ninad
Jilles Vreeken

Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with recurring seasonal trends, while also depending on geographical characteristics such as ecosystem variability. Existing methods for discovering causal graphs from time series either assume stationarity, do not permit both temporal and spatial distribution changes, or are unaware of locations with the same causal relationships. In this work, we therefore unify the three tasks of causal graph discovery in the non-stationary multi-context setting, of reconstructing temporal regimes, and of partitioning datasets and time intervals into those where invariant causal relationships hold. To construct a consistent score that forms the basis of our method, we employ the Minimum Description Length principle. Our resulting algorithm SPACETIME simultaneously accounts for heterogeneity across space and non-stationarity over time. Given multiple time series, it discovers regime changepoints and a temporal causal graph using non-parametric functional modeling and kernelized discrepancy testing. We also show that our method provides insights into real-world phenomena such as river-runoff measured at different catchments and biosphere-atmosphere interactions across ecosystems.

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NeurIPS Conference 2024 Conference Paper

Causal discovery with endogenous context variables

Wiebke Günther
Oana-Iuliana Popescu
Martin Rabel
Urmi Ninad
Andreas Gerhardus
Jakob Runge

Systems with variations of the underlying generating mechanism between different contexts, i. e. , different environments or internal states in which the system operates, are common in the real world, such as soil moisture regimes in Earth science. Besides understanding the shared properties of the system, in practice, the question of context-specific properties, i. e. , the change in causal relationships between contexts, arises. For real-world data, contexts are often driven by system variables, e. g. , precipitation highly influences soil moisture. Nevertheless, this setup needs to be studied more. To account for such endogenous contexts in causal discovery, our work proposes a constraint-based method that can efficiently discover context-specific causal graphs using an adaptive testing approach. Our approach tests conditional independence on the pooled datasets to infer the dependence between system variables, including the context, to avoid introducing selection bias. To yield context-specific insights, conditional independence is tested on context-specific data. We work out the theoretical framework for this adaptive testing approach and give a detailed discussion of the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. A simulation study to evaluate numerical properties shows that our approach behaves as expected, but also leads to a further understanding of current limitations and viable extensions.

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TMLR Journal 2024 Journal Article

Invariance & Causal Representation Learning: Prospects and Limitations

Simon Bing
Tom Hochsprung
Jonas Wahl
Urmi Ninad
Jakob Runge

Learning causal representations without assumptions is known to be fundamentally impossible, thus establishing the need for suitable inductive biases. At the same time, the invariance of causal mechanisms has emerged as a promising principle to address the challenge of out-of-distribution prediction which machine learning models face. In this work, we explore this invariance principle as a candidate assumption to achieve identifiability of causal representations. While invariance has been utilized for inference in settings where the causal variables are observed, theoretical insights of this principle in the context of causal representation learning are largely missing. We assay the connection between invariance and causal representation learning by establishing impossibility results which show that invariance alone is insufficient to identify latent causal variables. Together with practical considerations, we use our results to reflect generally on the commonly used notion of identifiability in causal representation learning and potential adaptations of this goal moving forward.

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UAI Conference 2023 Conference Paper

Causal Discovery for time series from multiple datasets with latent contexts

Wiebke Günther
Urmi Ninad
Jakob Runge

Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time series of river runoff from different catchments. The local catchment systems then share certain causal parents, such as time-dependent large-scale weather over all catchments, but differ in other catchment-specific drivers, such as the altitude of the catchment. These drivers can be called temporal and spatial contexts, respectively, and are often partially unobserved. Pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables enables us to overcome such latent confounding of system variables. In this work, we present a non-parametric time series causal discovery method, J(oint)-PCMCI$^+$, that efficiently learns such joint causal time series graphs when both observed and latent contexts are present, including time lags. We present asymptotic consistency results and numerical experiments demonstrating the utility and limitations of the method.

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UAI Conference 2023 Conference Paper

Increasing effect sizes of pairwise conditional independence tests between random vectors

Tom Hochsprung
Jonas Wahl
Andreas Gerhardus
Urmi Ninad
Jakob Runge

A simple approach to test for conditional independence of two random vectors given a third random vector is to simultaneously test for conditional independence of every pair of components of the two random vectors given the third random vector. In this work, we show that conditioning on additional components of the two random vectors that are independent given the third one increases the tests’ effect sizes while leaving the validity of the overall approach unchanged. We leverage this result to derive a practical pairwise testing algorithm that first chooses tests with a relatively large effect size and then does the actual testing. We show both numerically and theoretically that our algorithm outperforms standard pairwise independence testing and other existing methods if the dependence within the two random vectors is sufficiently high.

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AAAI Conference 2023 Conference Paper

Vector Causal Inference between Two Groups of Variables

Jonas Wahl
Urmi Ninad
Jakob Runge

Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or groups of scalar variables. We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. Our method employs sparsity estimates of directed and undirected graphs and is based on two new principles for groupwise causal reasoning that we justify theoretically in Pearl's graphical model-based causality framework. Our theoretical considerations are complemented by two new causal discovery algorithms for causal interactions between two random vectors which find the correct causal direction reliably in simulations even if interactions are nonlinear. We evaluate our methods empirically and compare them to other state-of-the-art techniques.

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NeurIPS Conference 2022 Conference Paper

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery

Wiebke Günther
Urmi Ninad
Jonas Wahl
Jakob Runge

Conditional independence (CI) testing is frequently used in data analysis and machine learning for various scientific fields and it forms the basis of constraint-based causal discovery. Oftentimes, CI testing relies on strong, rather unrealistic assumptions. One of these assumptions is homoskedasticity, in other words, a constant conditional variance is assumed. We frame heteroskedasticity in a structural causal model framework and present an adaptation of the partial correlation CI test that works well in the presence of heteroskedastic noise, given that expert knowledge about the heteroskedastic relationships is available. Further, we provide theoretical consistency results for the proposed CI test which carry over to causal discovery under certain assumptions. Numerical causal discovery experiments demonstrate that the adapted partial correlation CI test outperforms the standard test in the presence of heteroskedasticity and is on par for the homoskedastic case. Finally, we discuss the general challenges and limits as to how expert knowledge about heteroskedasticity can be accounted for in causal discovery.

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